---
title: "Accelerometer data processing with GGIR"
output:
   html_document:
    toc : true
    number_sections: true
    toc_depth: 3
    toc_float: true #by turning this on, the table of contents moves to the left of the page.
urlcolor: blue
vignette: >
  %\VignetteIndexEntry{Accelerometer data processing with GGIR}
  \usepackage[utf8]{inputenc}
  %\VignetteEngine{knitr::rmarkdown}
editor_options:
  markdown:
    wrap: 72
---

```{r, echo=FALSE, out.width = "60%", out.extra='style="border: 0; padding:20px"'}
knitr::include_graphics("GGIR-MASTERLOGO-RGB.png")
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


You are now in the main GGIR vignette. See also the complementary vignettes on: [Cut-points](https://CRAN.R-project.org/package=GGIR/vignettes/CutPoints.html), [Day segment analyses](https://CRAN.R-project.org/package=GGIR/vignettes/TutorialDaySegmentAnalyses.html), [GGIR parameters](https://CRAN.R-project.org/package=GGIR/vignettes/GGIRParameters.html), [Embedding external functions (pdf)](https://CRAN.R-project.org/package=GGIR/vignettes/ExternalFunction.pdf), and [Reading ad-hoc csv file formats](https://CRAN.R-project.org/package=GGIR/vignettes/readmyacccsv.html).

# Introduction

## What is GGIR?

[GGIR](https://CRAN.R-project.org/package=GGIR) is an R-package to
process multi-day raw accelerometer data for physical activity and sleep
research. The term ***raw*** refers to data being expressed in
m/s<sup>2</sup> or gravitational acceleration as opposed to the previous
generation accelerometers which stored data in accelerometer brand
specific units. The signal processing includes automatic calibration,
detection of sustained abnormally high values, detection of non-wear and
calculation of average magnitude of dynamic acceleration based on a
variety of metrics. Next, GGIR uses this information to describe the
data per recording, per day of measurement, and (optionally) per segment
of a day of measurement, including estimates of physical activity,
inactivity and sleep. We published an overview paper of GGIR in 2019
[link](https://doi.org/10.1123/jmpb.2018-0063).

This vignette provides a general introduction on how to use GGIR and
interpret the output, additionally you can find a [introduction
video](https://youtu.be/RuFBCAqFJ2M) and a
[mini-tutorial](https://youtu.be/S8YPTrYNWdU) on YouTube. If you want to
use your own algorithms for raw data then GGIR facilitates this with
it's external function embedding feature, documented in a separate
vignette: [Embedding external functions in
GGIR](https://CRAN.R-project.org/package=GGIR). GGIR is increasingly
being used by research groups across the world. A non-exhaustive
overview of academic publications related to GGIR can be found
[here](https://github.com/wadpac/GGIR/wiki/Publication-list). R package
GGIR would not have been possible without the support of the
contributors listed in the author list at
[GGIR](https://CRAN.R-project.org/package=GGIR), with specific code
contributions over time since April 2016 (when GGIR development moved to
GitHub) shown
[here](https://github.com/wadpac/GGIR/graphs/contributors).

**Cite GGIR:**

When you use GGIR in publications do not forget to cite it properly as
that makes your research more reproducible and it gives credit to it's
developers. See paragraph on [Citing GGIR](#CitingGGIR) for details.

## Contributing, Support, and Keeping up to date

**How to contribute to the code?**

The development version of GGIR can be found on
[github](https://github.com/wadpac/GGIR), which is also where you will
find
[guidance](https://github.com/wadpac/GGIR/blob/master/CONTRIBUTING.md)
on how to contribute.

**How can I get service and support?**

GGIR is open source software and does not come with service or support
guarantees. However, as user-community you can help each other via the
[GGIR google
group](https://groups.google.com/forum/#!forum/RpackageGGIR) or the
[GitHub issue tracker](https://github.com/wadpac/GGIR/issues). Please
use these public platform rather than private e-mails such that other
users can learn from the conversations.

If you need dedicated support with the use of GGIR or need someone to
adapt GGIR to your needs then Vincent van Hees is available as
[independent consultant](https://www.accelting.com/).

**Training in R essentials and GGIR** We offer frequent online GGIR training courses. Check our dedicated training
[website](https://www.accelting.com/ggir-training/) with more details and
the option to book your training. Do you have questions about the
training or the booking process? Do not hesitate to contact us via:
[training\@accelting.com](mailto:training@accelting.com){.email}.

Also of interest may be the brief [free R introduction tutorial](https://www.accelting.com/updates/short-r-tutorial/).


**Change log**

Our log of main changes to GGIR over time can be found
[here](https://cran.r-project.org/package=GGIR/news/news.html).

# Setting up your work environment

## Install R and RStudio

[Download and install R](https://cran.r-project.org/)

[Download and install
RStudio](https://posit.co/products/open-source/rstudio/)

Install GGIR with its dependencies from CRAN. You can do this with one
command from the console command line:

```{R,eval=FALSE}
install.packages("GGIR", dependencies = TRUE)
```

Alternatively, to install the latest development version with the latest
bug fixes use instead:

```{R,eval=FALSE}
install.packages("remotes")
remotes::install_github("wadpac/GGIR")
```

Additionally, in some use-cases you will need to install one or multiple 
additional packages:

-   If you are working with Axivity, GENEActiv, or GENEA files, install
    the GGIRread package with `install.packages("GGIRread")`
-   If you are working with ActiGraph gt3x files, install
    the read.gt3x package with `install.packages("read.gt3x")`
-   If you want to derive Neishabouricounts (with `do.neishabouricounts = TRUE`),
    install the actilifecounts package with `install.packages("actilifecounts")`
-   If you want to derive circadian rhythm indicators using the [Cosinor analysis and Extended Cosinor analysis] 
    (with `cosinor = TRUE`), install the ActCR package with `install.packages("ActCR")`

## Prepare folder structure

1.  GGIR works with the following accelerometer brands and formats:
    -   [GENEActiv](https://activinsights.com/) .bin
    -   [Axivity](https://axivity.com/) AX3 and AX6 .cwa
    -   [ActiGraph](https://theactigraph.com) .csv and .gt3x (.gt3x
        only the newer format generated with firmware versions above
        2.5.0. Serial numbers that start with "NEO" or "MRA" and have
        firmware version of 2.5.0 or earlier use an older format of the
        .gt3x file). Note for Actigraph users: If you want to work with
        .csv exports via the commercial ActiLife software then note that
        you have the option to export data with timestamps. Please do
        not do this as this causes memory issues for GGIR. To cope with
        the absence of timestamps GGIR will calculate timestamps from
        the sample frequency, the start time and start date as presented
        in the file header.
    -   [Movisens](https://www.movisens.com/en/) with data stored in
        folders.
    -   Any other accelerometer brand that generates csv output, see
        documentation for functions `read.myacc.csv` and argument
        `rmc.noise` in the [GGIR function documentation
        (pdf)](https://CRAN.R-project.org/package=GGIR/GGIR.pdf).
    Note that functionality for the following file formats was part of GGIR but has
    been deprecated as it required a significant maintenance effort without 
    a clear use case or community support: (1) .bin for the Genea monitor by
    Unilever Discover, an accelerometer that was used for some studies between
    2007 and 2012) .bin, and (2) .wav files as can be exported by the 
    Axivity Ltd OMGUI software. Please contact us if you think
    these data formats should be facilitated by GGIR again and if you are interested
    in supporting their ongoing maintenance.

2.  All accelerometer data that needs to be analysed should be stored in
    one folder, or subfolders of that folder.
3.  Give the folder an appropriate name, preferable with a reference to
    the study or project it is related to rather than just 'data',
    because the name of this folder will be used later on as an
    identifier of the dataset.

## GGIR shell function

GGIR comes with a large number of functions and optional settings
(arguments) per functions.

To ease interacting with GGIR there is one central function, named
`GGIR`, to talk to all the other functions. In the past this function
was called `g.shell.GGIR`, but we decided to shorten it to `GGIR` for
convenience. You can still use `g.shell.GGIR` because `g.shell.GGIR` has
become a wrapper function around `GGIR` passing on all arguments to
`GGIR` and by that providing identical functionality.

In this paragraph we will guide you through the main arguments to `GGIR`
relevant for 99% of research. First of all, it is important to
understand that the GGIR package is structured in two ways.

Firstly, it has a computational structure of five parts which are
applied sequentially to the data, and that `GGIR` controls each of these
parts:

-   Part 1: Loads the data and stores derived features (aggregations)
    needed for the other parts. This is the time-consuming part. Once
    this is done, parts 2-5 can be run (or re-run with different
    parameters in parts 2-5) relatively quickly.
-   Part 2: Data quality analyses and low-level description of signal
    features per day and per file. At this point a day is defined from
    midnight to midnight
-   Part 3: Estimation of sustained inactivity and sleep periods, needed
    for input to Part 4 for sleep detection
-   Part 4: Labels the sustained inactive periods detected in Part 3 as
    sleep, or daytime sustained inactivity, per night and per file
-   Part 5: Derives sleep and physical activity characteristics by
    re-using information derived in part 2, 3 and 4. Total time in
    intensity categories, the number of bouts, time spent in bouts and
    average acceleration (overall activity) is calculated.

The reason why it split up in parts is that it avoids having the re-do
all analysis if you only want to make a small change in the more
downstream parts. The specific order and content of the parts has grown
for historical and computational reasons.

Secondly, the function arguments which we will refer to as input
parameters are structured thematically independently of the five parts
they are used in:

-   params_rawdata: parameters related to handling the raw data such as
    resampling or calibrating
-   params_metrics: parameters related to aggregating the raw data to
    epoch level summary metrics
-   params_sleep: parameters related to sleep detection
-   params_physact: parameters related to physical activity
-   params_247: parameters related to 24/7 behaviours that do not fall
    into the typical sleep or physical activity research category.
-   params_output: parameters relating to how and whether output is
    stored.
-   params_general: general parameters not covered by any of the above
    categories

This structure was introduced in GGIR version 2.5-6 to make the GGIR
code and documentation easier to navigate.

To see the parameters in each parameter category and their default
values do:

```{R,eval=FALSE}
library(GGIR)
print(load_params())
```

If you are only interested in one specific category like sleep:

```{R,eval=FALSE}
library(GGIR)
print(load_params()$params_sleep)
```

If you are only interested in parameter "HASIB.algo" from the
sleep_params object:

```{R,eval=FALSE}
library(GGIR)
print(load_params()$params_sleep[["HASIB.algo"]])
```

Documentation for all arguments in the parameter objects can be found the vignette:
[GGIR configuration parameters](https://cran.r-project.org/package=GGIR).

All of these arguments are accepted as argument to function `GGIR`,
because `GGIR` is a shell around all GGIR functionality.
However, the `params_` objects themselves can not be provided as input to `GGIR`.

### Key general arguments

You will probably never need to think about most of the arguments listed
above, because a lot of arguments are only included to facilitate
methodological studies where researchers want to have control over every
little detail. See previous paragraph for links to the documentation and
how to find the default value of each parameter.

The bare minimum input needed for `GGIR` is:

```{R,eval=FALSE}
library(GGIR)
GGIR(datadir="C:/mystudy/mydata",
 outputdir="D:/myresults")
```

Argument `datadir` allows you to specify where you have stored your
accelerometer data and `outputdir` allows you to specify where you would
like the output of the analyses to be stored. This cannot be equal to
`datadir`. If you copy paste the above code to a new R script (file
ending with .R) and Source it in R(Studio) then the dataset will be
processed and the output will be stored in the specified output
directory.

Below we have highlighted the key arguments you may want to be aware of.
We are not giving a detailed explanation, please see the package manual
for that.

-   `mode` - which part of GGIR to run, GGIR is constructed in five
    parts.
-   `overwrite` - whether to overwrite previously produced milestone
    output. Between each GGIR part, GGIR stores milestone output to ease
    re-running parts of the pipeline.
-   `idloc` - tells GGIR where to find the participant ID (default:
    inside file header)
-   `strategy` - informs GGIR how to consider the design of the
    experiment.
    -   If `strategy` is set to value 1, then check out arguments
        `hrs.del.start` and `hrs.del.end`.
    -   If `strategy` is set to value 3 or 5, then check out arguments
        `ndayswindow`, `hrs.del.start` and `hrs.del.end`.
-   `maxdur` - maximum number of days you expect in a data file based on
    the study protocol.
-   `desiredtz` - time zone of the experiment.
-   `chunksize` - a way to tell GGIR to use less memory, which can be
    useful on machines with limited memory.
-   `includedaycrit` - tell GGIR how many hours of valid data per day
    (midnight-midnight) is acceptable.
-   `includenightcrit` - tell GGIR how many hours of a valid night
    (noon-noon) is acceptable.
-   `qwindow` - argument to tell GGIR whether and how to segment the day
    for day-segment specific analysis.
-   `mvpathreshold` and `boutcriter` - acceleration threshold and bout
    criteria used for calculating time spent in MVPA (only used in GGIR
    part2).
-   `epochvalues2csv` - to export epoch level magnitude of acceleration
    to a csv files (in addition to already being stored as RData file)
-   `dayborder` - to decide whether the edge of a day should be other
    than midnight.
-   `iglevels` - argument related to intensity gradient method proposed
    by A. Rowlands.
-   `do.report` - specify reports that need to be generated.
-   `viewingwindow` and `visualreport` - to create a visual report, this
    only works when all five parts of GGIR have successfully run.
    Note that the visual report was initially developed to provide something to show to
    study participants and not for data quality checking purposes. Over time we have improved
    the visual report to also be useful for QC-ing the data. however, some of
    the scorings as shown in the visual report are created for the visual report only and
    may not reflect the scorings in the main GGIR analyses as reported in the
    quantitative csv-reports. Most of our effort in the past 10 years has gone 
    into making sure that the csv-report are correct, while the visualreport has
    mostly been a side project. This is unfortunate and we hope to find funding
    in the future to design a new report specifically for the purpose of
    QC-ing the anlayses done by GGIR.
-   `maxRecordingInterval` - if specified controls whether neighboring or
    overlapping recordings with the same participant ID and brand are appended
    at epoch level. This can be useful when the intention is to monitor behaviour
    over larger periods of time but accelerometers only allow for a few weeks of 
    data collection. GGIR will never append or alter the raw input file, this operation
    is preformed on the derived data.

### Key arguments related to sleep analysis {#key-arguments-related-to-sleep-analysis}

For an explanation on how sleep is detected and the specific role of the
various function arguments see section [Sleep
analysis](#Sleep_analysis).

-   Arguments related to configuring the sleep detection algorithm:
    `anglethreshold`, `timethreshold`, `HASPT.algo`, `HASIB.algo`,
    `Sadeh_axis`, and `HASPT.ignore.invalid`.
-   `ignorenonwear` if set to TRUE then ignore detected monitor non-wear
    periods in the detection of sustained inactivity bouts to avoid
    confusion between monitor non-wear time.
-   If you want to create a visualisation of how sleep period time and
    sustained inactivity bouts match throughout a day then consider
    arguments `do.visual`, `outliers.only`, and `criterror`.
-   If you want to exclude the first and last night from the sleep
    analysis then used `excludefirstlast`.
-   `def.noc.sleep` specifies how the sleep period time window should be
    estimated if no sleeplog is used.
-   `includenightcrit` Minimum number of valid hours per night (24 hour
    window between noon and noon or 6pm-6pm).
-   `data_cleaning_file` to ginore specific nights for specific
    individuals, see also section [Data cleaning
    file](#Data_cleaning_file).
-   If you want the sleep analysis to be guided by a sleeplog (diary)
    then check out arguments `loglocation` which specifies the location
    of the spreadsheet (csv) with sleep log information. Further, use
    arguments `colid` and `coln1` to specify
    the details of your sleep log structure.

GGIR facilitates two possible sleeplog file structures:

#### Basic sleep log

Example of a basic sleeplog:

| ID  | onset_N1 | wakeup_N1 | onset_N2 | wakeup_N2 | onset_N3 | wakeup_N3 | onset_N4 | ... |
|-----|----------|-----------|----------|-----------|----------|-----------|----------|-----|
| 123 | 22:00:00 | 09:00:00  | 21:00:00 | 08:00:00  | 23:45:00 | 06:30:00  | 00:00:00 | ... |
| 345 | 21:55:00 | 08:47:00  |          |           | 23:45:00 | 06:30:00  | 00:00:00 | ... |

-   One column for participant id, this does not have to be the first
    column. Specify which column it is with argument `colid`.
-   Alternatingly one column for onset time and one column for waking
    time. Specify which column is the column for the first night by
    argument `coln1`, in the above example `coln1=2`.
-   Timestamps are to be stored without date as in hh:mm:ss with hour
    values ranging between 0 and 23 (not 24). If onset corresponds to
    lights out or intention to fall asleep, then it specify
    `sleepwindowType = "TimeInBed"`.
-   There can be multiple sleeplogs in the same spreadsheet. Each row
    representing a single recording.
-   First row: The first row of the spreadsheet needs to be filled with
    column names. For the basic sleep log format it does not matter what
    these column names are.

#### Advanced sleep log

Example of an advanced sleeplog for two recordings:

| ID  | D1_date    | D1_wakeup | D1_inbed | D1_nap_start | D1_nap_end | D1_nonwear1_off | D1_nonwear1_on | D2_date    | ... |
|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|
| 123 | 30/03/2015 | 09:00:00  | 22:00:00 | 11:15:00     | 11:45:00   | 13:35:00        | 14:10:00       | 31/03/2015 | ... |
| 567 | 20/04/2015 | 08:30:00  | 23:15:00 |              |            |                 |                | 21/04/2015 | ... |

Relative to the basic sleeplog format the advanced sleep log format
comes with the following changes:

-   Recording are stored in rows, while all information per days are
    stored in columns.
-   Information per day is preceded by one columns that holds the
    calendar date. GGIR has been designed to recognise and handle any
    date format but assumes that you have used the same date format
    consistently through the sleeplog.
-   Per calendar date there is a column for wakeup time and followed 
    by a column for onset or in-bed time. Note that this is different 
    from the basic sleep log, where wakeup time follows the column for 
    onset or in-bed time. So, the advanced sleep log is calendar date 
    oriented: asking the participant when they woke up and when the fell 
    asleep on a certain date. However, if the sleep onset time is at 
    2am, you should still fill in the 02:00:00, even though it is the 
    02:00:00 of the next calendar date.
-   You can add columns relating to self-reported napping time and nonwear time. These
    are not used for the sleep analysis in g.part3 and g.part4, but used
    in g.part5 to facilitate napping analysis, see argument
    `do.sibreport` and the paragraph on naps. Multiple naps and multiple 
    nonwear periods can be
    entered per day.
-   Leave cells for missing values blank.
-   Column names are critical for the advanced sleeplog format: Date
    columns are recognised by GGIR as any column name with the word
    "date" in it. The advanced sleep log format is recognised by GGIR by
    looking out for the occurrence of at least two columnnames with the
    word "date" in their name. Wakeup times are recognised with the
    words "wakeup" in the column name. Sleeponset, to bed or in bed
    times are recognised as the columns with any of the following
    character combinations in their name: "onset", "inbed", "tobed", or
    "lightsout". Napping times are recognised by columns with the word
    "nap" in their name. Nonwear times are recognised by columns with
    the word "nonwear" in their name.

### Key arguments related to time use analysis

For an explanation on how time use analysis is performed see section
[Waking-waking or 24 hour time-use analysis](#Waking-waking_or_24_hour).

-   `excludefirstlast.part5` - whether to ignore the last and first day.
-   `includedaycrit.part5` - tell GGIR what fraction of the waking hours
    in a day (value below 1) is acceptable.
-   `minimum_MM_length.part5` - tell GGIR what the minimum length
    (hours) should be of the MM window in part 5.
-   Configure thresholds for acceleration levels (some may want to
    interpret this as intensity levels): `threshold.lig`,
    `threshold.mod`, `threshold.vig`.
-   Configure what fraction of a bout needs to meet the threshold
    (cut-point) crtieria `boutcriter.in`, `boutcriter.lig`,
    `boutcriter.mvpa`. Note that boutcriter.mvpa = 0.8 means that an
    MVPA bout can have interruptions
    (i.e., the time out of MVPA intensity) that meet the following
    criteria:
    (1) A single interruption can last \< 1 min
    (2) Repeated interruptions are allowed provided that their total
        time does not exceed 20% of the bout duration
    (3) The time spent in the interruptions is included in the duration
        of the MVPA bout. For example: A 25-minute bout can have two 1
        minute interruption, but not a single 2-minute interruption.
        Here, the full 25 minutes would count towards the duration of
        the MVPA bout.
-   `timewindow` to specify whether days should be defined from midnight
    to midnight `"MM"`, from waking-up to waking-up `"WW"`, from sleep onset
    to sleep onset `"OO"`, or any combination of them.
-   Configure durations of bouts: `boutdur.mvpa`, `boutdur.in`, and
    `boutdur.lig`. Note that this can be a vector of multiple values
    indicating the minimum and maximum duration of subsequent bout
    types, e.g. 1-5 minutes MVPA, 5-10 minutes MVPA, and longer than 10
    minutes MVPA.

### Published cut-points and how to use them

This section has been rewritten and moved. Please, visit the vignette
[Published cut-points and how to use them in GGIR](https://cran.r-project.org/package=GGIR)
for more details on the cut-points available, how to use them, and some
additional reflections on the use of cut-points in GGIR.

### Example call

If you consider all the arguments above you me may end up with a call to
`GGIR` that could look as follows.

```{R,eval=FALSE}
library(GGIR)
GGIR(mode=c(1,2,3,4,5),
      datadir="C:/mystudy/mydata",
      outputdir="D:/myresults",
      do.report=c(2,4,5),
      #=====================
      # Part 2
      #=====================
      strategy = 1,
      hrs.del.start = 0,          hrs.del.end = 0,
      maxdur = 9,                 includedaycrit = 16,
      qwindow=c(0,24),
      mvpathreshold =c(100),
      excludefirstlast = FALSE,
      includenightcrit = 16,
      #=====================
      # Part 3 + 4
      #=====================
      def.noc.sleep = 1,
      outliers.only = TRUE,
      criterror = 4,
      do.visual = TRUE,
      #=====================
      # Part 5
      #=====================
      threshold.lig = c(30), threshold.mod = c(100),  threshold.vig = c(400),
      boutcriter = 0.8,      boutcriter.in = 0.9,     boutcriter.lig = 0.8,
      boutcriter.mvpa = 0.8, boutdur.in = c(1,10,30), boutdur.lig = c(1,10),
      boutdur.mvpa = c(1),
      includedaycrit.part5 = 2/3,
      #=====================
      # Visual report
      #=====================
      timewindow = c("WW"),
      visualreport=TRUE)
```

Once you have used `GGIR` and the output directory (outputdir) will be
filled with milestone data and results.

### Configuration file

Function `GGIR` stores all the explicitly entered argument values and
default values for the argument that are not explicitly provided in a
csv-file named config.csv stored in the root of the output folder. The
config.csv file is accepted as input to `GGIR` with argument
`configfile` to replace the specification of all the arguments, except
`datadir` and `outputdir`, see example below.

```{R,eval=FALSE}
library(GGIR)
GGIR(datadir="C:/mystudy/mydata",
             outputdir="D:/myresults", configfile = "D:/myconfigfiles/config.csv")
```

The practical value of this is that it eases the replication of
analysis, because instead of having to share you R script, sharing your
config.csv file will be sufficient. Further, the config.csv file
contribute to the reproducibility of your data analysis.

Note 1: When combining a configuration file with explicitly provided
argument values, the explicitly provided argument values will overrule
the argument values in the configuration file. Note 2: The config.csv
file in the root of the output folder will be overwritten every time you
use `GGIR`. So, if you would like to add annotations in the file, e.g.
in the fourth column, then you will need to store it somewhere outside
the output folder and explicitly point to it with `configfile` argument.

# Time for action: How to run your analysis?

## From the R console on your own desktop/laptop

Create an R-script and put the GGIR call in it. Next, you can source the R-script
with the `source` function in R:

`source("pathtoscript/myshellscript.R")`

or use the Source button in RStudio if you use RStudio.

## In a cluster

GGIR by default support multi-thread processing, which can be turned off
by seting argument `do.parallel = FALSE`. If this is still not fast
enough then we advise using a GGIR on a computing cluster. The way we did
it on a Sun Grid Engine cluster is shown below, please note that some of
these commands are specific to the computing cluster you are working on.
Also, you may actually want to use an R package like clustermq or
snowfall, which avoids having to write bash script. Please consult your
local cluster specialist to tailor this to your situation. In our case, we
had three files for the SGE setting:

**submit.sh**

```{bash,eval=FALSE}
for i in {1..707}; do
    n=1
    s=$(($(($n * $[$i-1]))+1))
    e=$(($i * $n))
    qsub /home/nvhv/WORKING_DATA/bashscripts/run-mainscript.sh $s $e
done
```

**run-mainscript.sh**

```{bash,eval=FALSE}
#! /bin/bash
#$ -cwd -V
#$ -l h_vmem=12G
/usr/bin/R --vanilla --args f0=$1 f1=$2 < /home/nvhv/WORKING_DATA/test/myshellscript.R
```

**myshellscript.R**

```{R,eval=FALSE}
options(echo=TRUE)
args = commandArgs(TRUE)
if(length(args) > 0) {
  for (i in 1:length(args)) {
    eval(parse(text = args[[i]]))
  }
}
GGIR(f0=f0,f1=f1,...)
```

You will need to update the `...` in the last line with the arguments
you used for `GGIR`. Note that `f0=f0,f1=f1` is essential for this to
work. The values of `f0` and `f1` are passed on from the bash script.

Once this is all setup you will need to call `bash submit.sh` from the
command line.

With the help of computing clusters GGIR has successfully been run on some of the
worlds largest accelerometer data sets such as UK Biobank and German NAKO study.

## Processing time

The time to process a typical seven day recording should be anywhere in between 
3 and 10 minutes depending on the sample frequency of the
recording, the sensor brand, data format, the exact configuration of GGIR, and 
the specifications of your computer. If you are observing processing times of 20
minutes or longer for a 7 day recording then probably you are slowed down by other 
factors. 

Some tips on how you may be able to address this:

- Make sure the data you process is on the same machine as where GGIR is run.
Processing data located somewhere else on a computer network can substantially 
slow software down.
- Make sure your machine has 8GB or more RAM memory, using GGIR on old machines with
only 4GB is known to be slow. However, total memory is not the only bottle neck, also 
consider the number of processes (threads) your CPU can run relative to the 
amount of memory. Ending up with 2GB per process seems a good target.
- Avoid doing other computational activities with your machine while running GGIR. 
For example, if you use DropBox or OneDrive make sure they do not sync while you
are running GGIR. When using GGIR to process large datasets it is probably best to
not use the machine, but make sure the machine is configured not to fall
asleep as that would terminate the analyses.

# Inspecting the results

GGIR generates the following types of output. - csv-spreadsheets with
all the variables you need for physical activity, sleep and circadian
rhythm research - Pdfs with on each page a low resolution plot of the
data per file and quality indicators - R objects with milestone data -
Pdfs with a visual summary of the physical activity and sleep patterns
as identified (see example below)

```{r, out.width = "700px",echo=FALSE}
knitr::include_graphics("reportexample.jpg")
```

## Output part 2 {.tabset}

Part 2 generates the following output:

-   part2_summary.csv: Person level summary (see below)
-   part2_daysummary.csv: Day level summary (see below)
-   QC/data_quality_report.csv: Overview of calibration results and
    whether or not a file was corrupt or too short to be processed,
-   QC/plots to check data quality 1.pdf: A pdf with visualisation of
    the acceleration time series in 15 minute resolution and with
    invalid data segments highlighted in colours (yellow: non-wear based
    on standard deviation threshold, brown: non-wear after extra
    filtering step (introduced in 2013), and purple: clipping)

### Person level summary

| (Part of) variable name | Description          |
|-------------------------|-----------------------------------------------|
| ID                      | Participant id       |
| device_sn               | Device serial number |
| bodylocation            | Body location extracted from file header      |
| filename                | Name of the data file|
| start_time              | Timestamp when recording started              |
| startday                | Day of the week on which recording started    |
| samplefreq              | Sample frequency (Hz)|
| device                  | Accelerometer brand, e.g. GENEACtiv           |
| clipping_score          | The Clipping score: Fraction of 15 minute windows per file for which the acceleration in one of the three axis was close to the maximum for at least 80% of the time. This should be 0.          |
| meas_dur_dys            | Measurement duration (days)                   |
| complete_24hcycle       | Completeness score: Fraction of 15 minute windows per 24 hours for which no valid data is available at any day of the measurement.           |
| meas_dur_def_proto_day  | measurement duration according to protocol (days): Measurement duration (days) minus the hours that are ignored at the beginning and end of the measurement motivated by protocol design         |
| wear_dur_def_proto_day  | wear duration duration according to protocol (days): So, if the protocol was seven days of measurement, then wearing the accelerometer for 8 days and recording data for 8 days will still make that the wear duration is 7 days                  |
| calib_err                      | Calibration error (static estimate) Estimated based on all 'non-movement' periods in the measurement after applying the autocalibration.    |
| calib_status            | Calibration status: Summary statement about the status of the calibration error minimisation   |
| ENMO_fullRecordingMean  | ENMO is the main summary measure of acceleration. The value presented is the average ENMO over all the available data normalised per 24-hour cycles (diurnal balanced), with invalid data imputed by the average at similar time points on different days of the week. In addition to ENMO it is possible to extract other acceleration metrics (i.e. BFEN, HFEN, HFENplus). We emphasize that it is calculated over the full recording because the alternative is that a variable is only calculated overmeasurement days with sufficient valid hours of data.          |
| ENMO                     | (only available if set to true in part1.R) ENMO is the main summary measure of acceleration. The value presented is the average ENMO over all the available data normalised per 24 hour cycles, with invalid data imputed by the average at similar timepoints on different days of the week. In addition to ENMO it is possible to extract other acceleration metrics in part1.R (i.e. BFEN, HFEN, HFENplus) See also [van Hees PLoSONE April 2013](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061691) for a detailed description and comparison of these techniques. |
| pX_A\_mg\_0-24h_fullRecording               | This variable represents the Xth percentile in the distribution of short epoch metric value A of the average day. The average day may not be ideal for describing the distribution. Therefore, the code also extracts the following variable.       |
| AD_pX_A\_mg_0-24h              | This variable represents the Xth percentile in the distribution of short epoch metric value A per day averaged across all days.                    |
| L5_A\_mg_0-24                  | Average of metric A during the least active five\* hours in the day that is the lowest rolling average value of metric A. (\* window size is modifiable by argument `winhr`)                        |
| M5_A\_mg_0-24                  | Average of metric A during the most active five\* hours in the day that is the lowest rolling average value of metric A. (\* window size is modifiable by argument `winhr`)                      |
| L5hr_A\_mg_0-24                | Starting time in hours and fractions of hours of L5_A\_mg_0-24   |
| M5hr_A\_mg_0-24                | Starting time in hours and fractions of hours of M5_A\_mg_0-24     |
| ig_gradient_ENMO_0 -24hr_fullRecording      | Intensity gradient calculated over the full recording.   |
| 1to6am_ENMO_mg     | Average metric value ENMO between 1am and 6am        |
| N valid WEdays     | Number of valid weekend days|
| N valid WKdays     | Number of valid week days       |
| IS\_interdailystability        | inter daily stability. The movement count that is derived for this was an attempt to follow the original approach by Eus J. W. Van Someren (Chronobiology International. 1999. Volume 16, issue 4).          |
| IV_in tradailyvariability      | intra daily variability. In contrast to the original paper, we ignore the epoch transitions between the end of a day and the beginning of the next day for the numerator of the equation, this to make it a true measure of intradaily variability. Same note as for IS: The movement count that is derived for this was an attempt to follow the original approach.           |
| IVIS\_windowsize_minutes       | Sizes of the windows based on which IV and IS are calculated (note that this is modifiable)    |
| IVIS_epochsize_seconds         | Argument has been deprecated  |
| `AD_` | All days (plain average of all available days, no weighting). The variable was calculated per day and then averaged over all the available days |
| `WE_` | Weekend days (plain average of all available days, no weighting). The variable was calculated per day and then averaged over weekend days only        |
| `WD_` | Week days (plain average of all available days, no weighting). The variable was calculated per day and then averaged over week days only     |
| `WWE_`| Weekend days (weighted average) The variable was calculated per day and then averaged over weekend days. Double weekend days are averaged. This is only relevant for experiments that last for more than seven days. |
| `WWD_`| Week days (weighted average) The variable was calculated per day and then averaged over week days. Double week days were averaged. This is only relevant for experiments that last for more than seven days)      |
| WWD\_MVPA_E5S_T100_ENMO       | Time spent in moderate-to-vigorous based on 5 second epoch size and an ENMO metric threshold of 100         |
| `WWE_MVPA_E5 S_B1M80%_T100_ENMO`| Time spent in moderate-to-vigorous based on 5 second epoch size and an ENMO metric threshold of 100 based on a bout criteria of 100    |
| `WE_[100, 150)_mg_0-24h_ENMO`  | Time spent between (and including) 100 mg and 150 (excluding 150 itself) between 0 and 24 hours (the full day) using metric ENMO data exclusion strategy (value=1, ignore specific hours; value=2, ignore all data before the first midnight and after the last midnight)    |
| `_M VPA_E5S_B1M80_T100`        | MVPA calculated based on 5 second epoch setting bout duration 1 Minute and inclusion criterion of more than 80 percent. This is only done for metric ENMO at the moment, and only if mvpa threshold is not left blank     |
| `_ENMO_mg`         | ENMO or other metric was first calculated per day and then average according to AD, WD, WWE, WWD           |
| data exclusion strategy        | A log of the decision made when calling g.impute: value=1 mean ignore specific hours; value=2 mean ignore all data before the first midnight and after the last midnight |
| n hours ignored at start of meas (if strategy=1, 3 or 5)        | number of hours ignored at the start of the measurement (if strategy = 1) or at the start of the `ndayswindow` (if strategy = 3 or 5) A log of decision made in part2.R       |
| n hours ignored at end of meas (if strategy=1, 3 or 5)          | number of hours ignored at the end of the measurement (if strategy = 1) or at the end of the `ndayswindow` (if strategy = 3 or 5). A log of decision made in part2.R         |
| n hours ignored at end of meas (if strategy = 1, 3 or 5)          | number of days of measurement after which all data is ignored (if strategy = 1, 3 or 5) A log of decision made in part2.R    |
| epoch size to which acceleration was averaged (seconds) | A log of decision made in part1.R     |
| pdffilenumb        | Indicator of in which pdf-file the plot was stored  |
| pdfpagecount       | Indicator of in which pdf-page the plot was stored|
| `cosinor_`         | Cosinor analysis estimates such as mes, amp, acrophase, and acrotime, as documented in the [Ac tCR](https://CRAN.R-project.org/package=ActCR) package.   |
| `cosinorExt_`      | Extended Cosinor analysis estimates such as minimum, amp, alpha, beta, acrotime, UpMesor, DownMesor, MESOR, and F_pseudo, as documented in the [Ac tCR](https://CRAN.R-project.org/package=ActCR) package.    |
| `cosinorIV`        | Cosinor analysis compatible estimate of the Intradaily Variability (IV)        |
| `cosinorIS`        | Cosinor analysis compatible estimate of Interdaily Stability (IS)  |

### Day level summary

This is a non-exhaustive list, because most concepts have been explained
in summary.csv

| (Part of) variable name    | Description  |
|--------------------------|----------------------------------------------|
| ID| Participant id           |
| filename       | Name of the data file    |
| calender_date  | Timestamp and date on which measurement started   |
| bodylocation   | Location of the accelerometer as extracted from file header    |
| N valid hours  | Number of hours with valid data in the day        |
| N hours        | Number of hours of measurement in a day, which typically is 24, unless it is a day on which the clock changes (DST) resulting in 23 or 25 hours. The value can be less than 23 if the measurement started or ended this day      |
| weekday        | Name of weekday          |
| measurement    | Day of measurement Day number relative to start of the measurement         |
| L5hr_ENMO_mg_0-24h         | Hour on which L5 starts for these 24 hours (defined with metric ENMO)      |
| L5_ENMO_mg_0-24h           | Average acceleration for L5 (defined with metric ENMO)         |
| `[A,B)_mg_0-24h_ENMO`      | Time spent in minutes between (and including) acceleration value A in mg and (excluding) acceleration value B in mg based on metric ENMO  |
| ig\_gradient_ENMO_0-24hr  | Gradient from intensity gradient analysis proposed by [Rowlands et al. 2018](https://journals.lww.com/acsm-msse/Fulltext/2018/06000/Beyond_Cut_%20Points__Accelerometer_Metrics_that.25.aspx) based on metric ENMO for the time segment 0 to 24 hours  |
| ig\_intercept_ENMO_0-24hr | Intercept from intensity gradient analysis proposed by [Rowlands et al. 2018](https://journals.lww.com/acsm-msse/Fulltext/2018/06000/Beyond_Cut_%20Points__Accelerometer_Metrics_that.25.aspx) based on metric ENMO for the time segment 0 to 24 hours |
| ig\_rsquared_ENMO_0-24hr  | r squared from intensity gradient analysis proposed by [Rowlands et al. 2018](https://journals.lww.com/acsm-msse/Fulltext/2018/06000/Beyond_Cut_%20Points__Accelerometer_Metrics_that.25.aspx) based on metric ENMO for the time segment 0 to 24 hours |

### Data_quality_report

The data_quality_report.csv is stored in subfolder folder results/QC.

| (Part of) variable name  | Description                                  |
|--------------------------|----------------------------------------------|
| filename                 | file name                                    |
| file.corrupt	           | Is file corrupt? TRUE or FALSE (mainly tested for GENEActiv bin files) |
| file.too.short	         | File too short for processing? ([definition](#Minimum_recording_duration)) TRUE or FALSE |
| use.temperature	| Temperature used for auto-calibration? TRUE or FALSE |
| scale.x	| Auto-calibration scaling coefficient for x-axis (same for y and z axis, not shown here) |
| offset.x	| Auto-calibration offset coefficient for x-axis (same for y and z axis, not shown here) |
| temperature.offset.x | Auto-calibration temperature offset coefficient for x-axis (same for y and z axis, not shown here) |
| cal.error.start	| Calibration error prior to auto-calibration |
| cal.error.end		| Calibration error after auto-calibration |
| n.10sec.windows	| Number of 10 second epochs used as sphere data in auto-calibration |
| n.hours.considered | Number of hours of data considered for auto-calibration |
| QCmessage	| Character QC message at the end of the auto-calibration |
| mean.temp	|  Mean temperature in sphere data |
| device.serial.number	| Device serial number |
| NFilePagesSkipped	| (Only for Axivity .cwa format) Number of data blocks skipped |
| filehealth_totimp_min | (Only for Axivity .cwa format) Total number of minutes of data imputed |
| filehealth_checksumfail_min	| (Only for Axivity .cwa format) Total number of minutes of data where the checksum failed |
| filehealth_niblockid_min |  (Only for Axivity .cwa format) Total number of minutes of data with non-incremental block ids |
| filehealth_fbias0510_min | (Only for Axivity .cwa format) Total number of minutes with a sampling frequency bias between 5 and 10% |
| filehealth_fbias1020_min | (Only for Axivity .cwa format) Total number of minutes with a sampling frequency bias between 10 and 20% |
| filehealth_fbias2030_min |  (Only for Axivity .cwa format) Total number of minutes with a sampling frequency bias between 20 and 30% |
| filehealth_fbias30_min | (Only for Axivity .cwa format) Total number of minutes with a sampling frequency bias higher than 30% |
| filehealth_totimp_N | (Only for Axivity .cwa format) Total number of data blocks that were imputed |
| filehealth_checksumfail_N	| (Only for Axivity .cwa format)  Total number of blocks where the checksum failed  |
| filehealth_niblockid_N | (Only for Axivity .cwa format) Total number of data blocks with non-incremental block ids  |
| filehealth_fbias0510_N | (Only for Axivity .cwa format) Total number of data blocks with a sampling frequency bias between 5 and 10% |
| filehealth_fbias1020_N | (Only for Axivity .cwa format) Total number of data blocks with a sampling frequency bias between 10 and 20%|
| filehealth_fbias2030_N | (Only for Axivity .cwa format) Total number of data blocks with a sampling frequency bias between 20 and 30% |
| filehealth_fbias30_N | (Only for Axivity .cwa format) Total number of data blocks with a sampling frequency bias higher than 30% |



## Output part 4 {.tabset}

Part 4 generates the following output:

### Night level summaries

-   part4_nightsummary_sleep_cleaned.csv
-   QC/part4_nightsummary_sleep_full.csv

The latter with '_full' in the name is intended to aid
clarifying why some nights (if any) are excluded from the cleaned summary report.
Although, nights where the accelerometer was not worn at all are excluded from this. So,
if you have a 30 day recording where the accelerometer was not worn from day 7 onward then
you will not find the last 22 nights in either csv-report.

The csv. files contain the variables as shown below.

| (Part of) variable name    | Description         |
|-------------------------|-----------------------------------------------|
| ID| Participant ID extracted from file           |
| night         | Number of the night in the recording         |
| sleeponset    | Detected onset of sleep expressed as hours since the midnight of the previous night.           |
| wakeup        | Detected waking time (after sleep period) expressed as hours since the midnight of the previous night.      |
| SptDuration   | Difference between onset and waking time.    |
| sleepparam    | Definition of sustained inactivity by accelerometer.      |
| guider        | guider used as discussed in paragraph [Sleep analysis](#Sleep_analysis).           |
| guider_onset  | Start of Sleep Period Time window derived from the guider.|
| guider_wake   | End of Sleep Period Time window derived guider.           |
| guider_SptDuration         | Time SPT duration derived from guider_wake and guider_onset.          |
| error_onset   | Difference between sleeponset and guider_onset|
| error_wake    | Difference between wakeup and guider_wake    |
| fraction_night_invalid     | Fraction of the night (noon-noon or 6pm-6pm) for which the data was invalid, e.g. monitor not worn or no accelerometer measurement started/ended within the night.        |
| SleepDurationInSpt         | Total sleep duration, which equals the accumulated nocturnal sustained inactivity bouts within the Sleep Period Time.   |
| duration_sib_wakinghours   | Accumulated sustained inactivity bouts during the day. These are the periods we would label during the night as sleep, but during the day they form a subclass of inactivity, which may represent day time sleep or wakefulness while being motionless for a sustained period of time number_sib_sleepperiod} Number of nocturnal sleep periods, with nocturnal referring to the Sleep Period Time window. |
| duration_sib_wakinghours_atleast15min  | Same as duration_sib_wakinghours, but limited to SIBs that last at least 15 minutes.           |
| num ber_sib_wakinghours    | Number of sustained inactivity bouts during the day, with day referring to the time outside the Sleep Period Time window.|
| sleeponset_ts | sleeponset formatted as a timestamp          |
| wakeup_ts     | wakeup formatted as a timestamp  |
| guider_onset_ts| guider_onset formatted as a timestamp        |
| guider_wake_ts| guider_wake formatted as a timestamp         |
| page          | pdf page on which the visualisation can be found          |
| daysleeper    | If 0 then the person is a nightsleeper (sleep period did not overlap with noon) if value=1 then the person is a daysleeper (sleep period did overlap with noon)           |
| weekday       | Day of the week on which the night started   |
| calendardate  | Calendar date on which the night started in day/month/year format.    |
| filename      | Name of the accelerometer file   |
| cleaningcode  | see paragraph [Cleaningcode](#Cleaningcode)  |
| sleeplog_used | Whether a sleep log was used (TRUE/FALSE)    |
| acc_available | Whether accelerometer data was available (TRUE/FALSE).    |
| WASO          | Wake After Sleep Onset: SptDuration - SleepDurationInSpt  |
| SptDuration   | Sleep Period Time window duration: wakeup - sleeponset    |
| error_onset   | Difference between sleeponset and guider_onset (this variable is only available in the full report as stored in the QC folder)       |
| error_wake    | Difference between wakeup and guider_wake (this variable is only available in the full report as stored in the QC folder)|
| SleepRegularityIndex       | The Sleep Regularity Index as proposed by [Phillips et al. 2017](https://www.nature.com/articles/s41598-017-03171-4), but calculated per day-pair to enable user to study patterns across days     |
| SriFractionValid           | Fraction of the 24 hour period that was valid in both current as well as in matching timestamps for the next calendar day. See GGIR function manual for details           |
| nonwear_perc_spt           | Non-wear percentage during the spt hours of this day. This is a copy of the nonwear_perc_spt calculated in [part 5](#output5), only included in part 4 reports if part 5 has been run with timewindow = WW      |


#### Non-default variables in part 4 csv report

These additional are only stored if you used a sleeplog that captures
time in bed, or when using guider HorAngle for hip-worn accelerometer
data. If either of these applies set argument `sleepwindowType` to
"TimeInBed".

| (Part of) variable name   | Description  |
|-------------------------|-----------------------------------------------|
| guider_guider_inbedStart  | Time of getting in bed   |
| guider_guider_inbedEnd    | Time of getting out of bed|
| guider_inbedDuration      | Time in Bed: guider_inbedEnd - guider_inbedStart  |
| sleepefficiency           | Sleep efficiency, calculated as: SleepDurationInSpt / guider_inbedDuration or SleepDurationInSpt / (Spt + latency) as defined with  `relyonguider_SleepEfficiency`|
| sleeplatency  | Sleep latency, calculated as: sleeponset - guider_inbedStart   |

### Person level summaries

-   part4_summary_sleep_cleaned.csv
-   QC/part4_summary_sleep_full.csv

In the person level report the variables are derived from the variables
in the night level summary. Minor extensions to the variable names
explain how variables are aggregated across the days. Please find below
extra clarification on a few of the variable names for which the meaning
may not be obvious:

| (Part of) variable name       | Description     |
|-------------------------|-----------------------------------------------|
| `_mn`| mean across days|
| `_sd`| standard deviation across days           |
| `_AD`| All days        |
| `_WE`| Weekend days    |
| `_WD`| Week days       |
| sleeplog_used     | Whether a sleeplog was available (TRUE) or not (FALSE)|
| sleep_efficiency  | Accelerometer derived sleep efficiency within the sleep period time calculated as the ratio between acc_SleepDurationInSpt and guider_SptDuration (denominator) or acc_SleepDurationInSpt and acc_SptDuration + latency (denominator), as defined with relyonguider_SleepEfficiency. Only available at person level, because at night level the user can calculate this from existing variables. |
| n_nights_acc      | Number of nights of accelerometer data   |
| n_nights_sleeplog | Number of nights of sleeplog data.       |
| n\_WE_nights_complete        | Number of weekend nights complete which means both accelerometer and estimate from guider. |
| n\_WD_nights_complete        | Number of weekday nights complete which means both accelerometer and estimate from guider. |
| n\_WEnights_daysleeper       | Number of weekend nights on which the person slept until after noon.           |
| n\_WDnights_daysleeper       | Number of weekday nights on which the person slept until after noon.           |
| duration_sib_wakinghour      | Total duration of sustained inactivity bouts during the waking hours.          |
| number_sib_wakinghours       | Number of sustained inactivity bouts during the waking hours.     |
| average\_dur_sib_wakinghours | Average duration of the sustained inactivity bouts during the day (outside the sleep period duration). Calculated as duration_sib_wakinghour divided by number_sib_wakinghours per day, after which the mean and standard deviation are calculated across days.           |

### visualisation_sleep.pdf

Visualisation to support data quality checks: - visualisation_sleep.pdf
(optional)

When input argument `do.visual` is set to TRUE GGIR can show the
following visual comparison between the time window of being asleep (or
in bed) according to the sleeplog and the detected sustained inactivity
bouts according to the accelerometer data. This visualisation is stored
in the results folder as `visualisation_sleep.pdf`.

Explanation of the image: Each line represents one night. Colours are
used to distinguish definitions of sustained inactivity bouts (2
definitions in this case) and to indicate existence or absence of
overlap with the sleeplog. When argument `outliers.only` is set to FALSE
it will visualise all available nights in the dataset. If
`outliers.only` is set to TRUE it will visualise only nights with a
difference in onset or waking time between sleeplog and sustained
inactivity bouts larger than the value of argument `criterror`.

This visualisation with outliers.only set to TRUE and critererror set to
4 was very powerful to identify entry errors in sleeplog data in van
Hees et al PLoSONE 2015. We had over 25 thousand nights of data, and
this visualisation allowed us to quickly zoom in on the most problematic
nights to investigate possible mistakes in GGIR or mistakes in data
entry.

```{r, out.width = "700px",echo=FALSE}
knitr::include_graphics("example_dovisual.jpg")
```

## Output part 5 {.tabset}

The output of part 5 is dependent on the parameter configuration, it
will generate as many output files as there are unique combination of
the three thresholds provided.

For example, the following files will be generated if the threshold
configuration was 30 for light activity, 100 for moderate and 400 for
vigorous activity: 

-   part5_daysummary_MM_L30M100V400_T5A5.csv 
-   part5_daysummary_WW_L30M100V400_T5A5.csv
-   part5_personsummary_MM_L30M100V400_T5A5.csv
-   part5_personsummary_WW_L30M100V400_T5A5.csv
-   file summary reports/Report_nameofdatafile.pdf


### Day level summary {#output5}

| (Term in) variable name | Description           |
|--------------------------|----------------------------------------------|
| sleeponset  | onset of sleep expressed in hours since the midnight in the night preceding the night of interest, e.g. 26 is 2am.        |
| wakeup      | waking up time express in the same way as sleeponset.       |
| sleeponset_ts           | onset of sleep expressed as a timestamp hours:minutes:seconds           |
| daysleeper  | if 0 then the person woke up before noon, if 1 then the person woke up after noon    |
| cleaningcode| See paragraph [Cleaningcode](#Cleaningcode).   |
| dur_day_spt_min         | Total length of daytime waking hours and spt combined (typically 24 hours for MM report).        |
| `dur_`      | duration of a behavioral class that will be specified int he rest of the variable name           |
| `ACC_`      | (average) acceleration according to default metric specific by acc.metric|
| `_spt_wake_`| Wakefulness within the Sleep period time window.|
| `_spt_sleep_`           | Sleep within the Sleep period time window.     |
| `_IN_`      | Inactivity. Note that we use the term inactivity instead
        of sedentary behaviour for the lowest intensity level of behaviour. The reason
        for this is that GGIR does not attempt to classifying the activity type 
        sitting at the moment, by which we feel that using the term sedentary
        behaviour would fail to communicate that.|
| `_LIG_`     | Light activity        |
| `_MOD_`     | Moderate activity     |
| `_VIG_`     | Vigorous activity     |
| `_MVPA_`    | Moderate or Vigorous activity      |
| `_unbt_`    | Unbouted  |
| `_bts_`     | Bouts (also known as sojourns), which are segments that for which the acceleration is within a specified range for a specified fraction of the time.|
| `_bts_1_10_`| Bouts lasting at least 1 minute and less than 10 minutes (1 and 9.99 minutes are included, but 10 minutes is not).        |
| Nblock      | number of blocks of a certain behavioral class, not these are not bouts but a count of the number of times the behavioral class occurs without interruptions.   |
| WW          | in filename refers to analyses based on the timewindow from waking to waking up      |
| MM          | in filename refers to analyses done on windows between midnight and midnight         |
| calendar_date           | calendar date on which the window started in day/month/year format. So, for WW window this could mean that you have two windows starting on the same date.      |
| weekday     | weekday on which the window started. So, for WW window this could mean that you have two windows starting on the weekday. |
| `_total_IN` | total time spent in inactivity (no distinction between bouted or unbouted behavior, this is a simple count of the number of epochs that meet the threshold criteria.        |
| `_total_LIG`| total time spent in light activity.|
| nonwear_perc_day        | Non-wear percentage during the waking hours of this day.    |
| nonwear_perc_spt        | Non-wear percentage during the spt hours of this day.       |
| nonwear_perc_day_spt    | Non-wear percentage during the whole day, including waking and spt.     |
| dur_day_min | Duration of waking hours within this day window|
| dur_spt_min | Duration of Sleep Period Time within this day window.       |
| dur_day_spt_min         | Duration this day window, including both waking hours and SPT.          |
| sleep_efficiency        | sleep_efficiency in part 5 is not the same as in part 4, but calculated as the percentage of sleep within the sleep period time window. The conventional approach is the approach used in part 4.    |
| L5TIME      | Timing of least active 5hrs, expressed as timestamp in the day          |
| M5TIME      | Timing of most active 5hrs         |
| L5TIME_num, M5TIME_num  | Timing of least/most active 5hrs, expressed as hours in the day. Note that L5/M5 timing variables are difficult to average across days because 23:00 and 1:00 would average to noon and not to midnight. So, caution is needed when interpreting person averages.   |
| L5VALUE     | Acceleration value for least active 5hrs       |
| M5VALUE     | Acceleration value for most active 5hrs        |
| `ig_`       | All variables related to intensity gradient analysis |
| `_gradient`   | Gradient from intensity gradient analysis proposed by [Rowlands et al. 2018](https://journals.lww.com/acsm-msse/Fulltext/2018/06000/Beyond_Cut_%20Points__Accelerometer_Metrics_that.25.aspx) for the waking hours window (`_day_`) and for the full window (`_day_spt_`)      |
| `_intercept`  | Intercept from intensity gradient analysis proposed by [Rowlands et al. 2018](https://journals.lww.com/acsm-msse/Fulltext/2018/06000/Beyond_Cut_%20Points__Accelerometer_Metrics_that.25.aspx) for the waking hours window (`_day_`) and for the full window (`_day_spt_`)      |
| `_rsquared`   | r squared from intensity gradient analysis proposed by [Rowlands et al. 2018](https://journals.lww.com/acsm-msse/Fulltext/2018/06000/Beyond_Cut_%20Points__Accelerometer_Metrics_that.25.aspx) for the waking hours window (`_day_`) and for the full window (`_day_spt_`)      |
| `FRAG_`     | All variables related to behavioural fragmentation analysis |
| `TP_`       | Transition probability|
| PA2IN       | Physical activity fragments followed by inactivity fragments|
| IN2PA       | Physical inactivity fragments followed by activity fragments|
| Nfrag       | Number of fragments   |
| IN2LIPA     | Inactivity fragments followed by LIPA          |
| IN2MVPA     | Inactivity fragments followed by MVPA          |
| `mean_dur`  | mean duration of a fragment category           |
| `Gini_dur`  | Gini index|
| `CoV_dur`   | Coefficient of Variation           |
| alpha       | Power law exponent    |
| `x0.5`      | Derived from power law exponent alpha, see [Chastin et al. 201 0](https://doi.org/10.1016/j.gaitpost.2009.09.002)        |
| `W0.5`      | Derived from power law exponent alpha, see [Chastin et al. 201 0](https://doi.org/10.1016/j.gaitpost.2009.09.002)        |
| nap_count   | Total number of naps, only calculated when argument do.sibreport = TRUE, currently optimised for 3.5-year olds. See function documentation for function `g.part5.classifyNaps` in the [GGIR function documentation (pdf)](https:/CRAN.R-project.org/package=GGIR/GGIR.pdf). |
| nap_totalduration       | Total nap duration, only calculated when argument do.sibreport = TRUE, currently optimised for 3.5-year old. See function documentation for function `g.part5.classifyNaps` in the [GGIR function documentation (pdf)](https:/CRAN.R-project.org/package=GGIR/GGIR.pdf).    |
| sibreport_n_items	| Only created if `do.sibreport = TRUE`. Number of items in the sibreport |
| sibreport_n_items_day |	Only created if `do.sibreport = TRUE`. Number of items in the sibreport for this specific day |
| nbouts_day_X | Only created if `do.sibreport = TRUE`. Number of bouts in a day of X where X can be sib (sustained inactivity bout), srnap (self-reported nap) or srnonw (self-reported nonwear) |
| noverl_X | Only created if `do.sibreport = TRUE`. Number of overlapping bouts in a day of X where X can be sib_srnap, sib_srnonw, srnap_sib, or srnonw_sib |
| frag_mean_dur_X_day | Only created if `do.sibreport = TRUE`. Mean duration of X per day, where X can be sib, srnap or srnonw |
| dur_day_X_min | Only created if `do.sibreport = TRUE`. Total duration in minutes of X per day, where X can be sib, srnap or srnonw |
| mdur_X_overl_Y | Only created if `do.sibreport = TRUE`. Mean duration of the overlap between X and Y, which are combinations of sib, srnap or srnonw |
| tdur_X_overl_Y | Only created if `do.sibreport = TRUE`. Total duration in minutes of the overlap between X and Y, which are combinations of sib, srnap or srnonw |
| perc_X_overl_Y | Only created if `do.sibreport = TRUE`. Percentage of overlap between X and Y, which are combinations of sib, srnap or srnonw |


**Special note if you are working on compositional data analysis:**

The duration of all `dur_` variables that have `_total_` in their name
should add up to the total length of the waking hours in a day.
Similarly, the duration of all other `dur_` variables excluding the
variables `_total_` in their name and excluding the variable with
`dur_day_min`, `dur_spt_min`, and `dur_day_spt_min` should also add up
to the length of the full day.

**Motivation for default boutcriter.in = 0.9:**

The idea is that if you allow for bouts of 30 minutes it would not make
sense to allow for breaks of 20 percent (6 minutes!) this is why I used
a more stringent criteria for the highest category. Please note that you
can change these criteria via arguments `boutcriter.mvpa`,
`boutcriter.in`, and `boutcriter.lig`.

### Person level summary

Most variables in the person level summary are derived from the day
level summary, but extended with `_pla` to indicate that the variable
was calculated as the plain average across all valid days. Variables
extended with `_wei` represent the weighted average of across all days
where weekend days always weighted 2/5 relative to the contribution of
week days.

| Variable name       | Description           |
|--------------------------|----------------------------------------------|
| Nvaliddays          | Total number of valid days.       |
| Nvaliddays_WD       | Number of valid week days.        |
| Nvaliddays_WE       | Number of valid weekend days, where the days that start on Saturday or Sunday are considered weekend.        |
| NcleaningcodeX      | Number of days that had cleaning code X for the corresponding sleep analysis in part 4. In case of MM analysis this refers to the night at the end of the day. |
| Nvaliddays_AL10F_WD | Number of valid week days with at least 10 fragments (5 inactivity or 5 inactive)   |
| Nvaliddays_AL10F_WE | Number of valid weekend days with at least 10 fragments (5 inactivity or 5 inactive)|
| `_wei`  | weighted average of weekend and week days, using a 2/5 ratio, see above.|
| `_pla`  | plain average of all days, see above           |

# Motivation and clarification

In this chapter we will try to collect motivations and clarification
behind GGIR which may not have been clear from the existing
publications.

## Reproducibilty of GGIR analyses

Some tips to increase reproducibility of your findings:

1.  When you publish your findings, please remember to add the GGIR
    package version number. All of GGIR are archived by CRAN and
    available from the archive section on the package
    [website](https://CRAN.R-project.org/package=GGIR). GGIR has evolved
    over the years. To get a better understanding of how versions differ
    you should check the NEWS sections from the package
    [website](https://CRAN.R-project.org/package=GGIR)
2.  Report how you configured the accelerometer
3.  Report the study protocol and wear instructions given to the
    participants
4.  Report GGIR version
5.  Report how GGIR was used: Share the config.csv file or your R script
6.  Report how you post-processed / cleaned GGIR output
7.  Report how reported outcomes relate to the specific variable names
    in GGIR

## Auto-calibration

An acceleration sensor works on the principle that acceleration is
captured mechanically and converted into an electrical signal. The
relationship between the electrical signal and the acceleration is
usually assumed to be linear, involving an offset and a gain factor. We
shall refer to the establishment of the offset and gain factor as the
sensor calibration procedure. Accelerometers are usually calibrated as
part of the manufacturing process under non-movement conditions using
the local gravitational acceleration as a reference. The manufacturer
calibration can later be evaluated by holding each sensor axis parallel
(up and down) or perpendicular to the direction of gravity; readings for
each axis should be ±1 and 0 g, respectively. However, this procedure
can be cumbersome in studies with a high throughput. Furthermore, such a
calibration check will not be possible for data that have been collected
in the past and for which the corresponding accelerometer device does
not exist anymore. Techniques have been proposed that can check and
correct for calibration error based on the collected triaxial
accelerometer data in the participant's daily life without additional
experiments, referred to as autocalibration. The general principle of
these techniques is that a recording of acceleration is screened for
nonmovement periods. Next, the moving average over the nonmovement
periods is taken from each of the three orthogonal sensor axes and used
to generate a three-dimensional ellipsoid representation that should
ideally be a sphere with radius 1 g. Here, deviations between the radius
of the three-dimensional ellipsoid and 1 g (ideal calibration) can then
be used to derive correction factors for sensor axis-specific
calibration error. This auto-calibration performed by GGIR uses this
technique and a more detailed description and demonstration can be found
in the published paper.

Reference:

-   van Hees VT, Fang Z, Langford J, Assah F, Mohammad A, da Silva IC,
    Trenell MI, White T, Wareham NJ, Brage S. Autocalibration of
    accelerometer data for free-living physical activity assessment
    using local gravity and temperature: an evaluation on four
    continents. J Appl Physiol (1985). 2014 Oct 1;117(7):738-44. PMID:
    25103964
    [link](https://doi.org/10.1152/japplphysiol.00421.2014)

Key decisions to be made:

1.  Whether to apply auto-calibration or not (default and recommended
    setting is YES). You can turn this off by setting argument `do.call`
    in `GGIR` to `do.call=FALSE`.
2.  Other variables are probably best left in their default setting

Key output variables:

1.  Variable value `cal.error.end` as stored in data_quality_report.csv
    or variable value calib_err in summary.csv. These should be less
    than 0.01 g (10mg).

## Non-wear detection

Accelerometer non-wear time is detected on the basis of statistics derived from 
a rolling time window. A step in time is classified as non-wear if 
both of the following criteria are met for at least two out of the three accelerometer axes:

- The standard deviation of the accelerations is less than accelerometer brand specific reference
values, which for most brands is 13.0 m*g*
($1 mg = 0.00981 m·s^−2$)
- The range of accelerations (i.e., maximum value *minus* minimum value) 
is less than 50 m*g*.

The size of the rolling time window and the size of the steps it takes in time are defined by
argument `windowsizes`, a vector with length three. More specifically, the second value 
(mediumsize window, default = 15 min) and the third value 
(longsize window, default = 60 min) are used.

How it then labels the data depends on the non-wear approach taken as discussed below,
and specified with argument `nonwear_approach`. At the moment there are two approaches to detect
non-wear: `nonwear_approach = "2013"` and `nonwear_approach = "2023"`.

### Approaches to detect non-wear: 2013 and 2023 algorithms

**nonwear_approach = "2013"**

The 2013 approach is a revision of the approach first described in the [2011 PLoSONE publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0022922).
It uses the criteria derived from the longsize window centered around each mediumsize 
window (default = 15 minutes) to classify each mediumsize window at its centre. 

The modification of the algorithm in 2013 relative to 2011 included:

-   The longsize window changed from 30 minutes to 60 minutes to decrease the chance of
accidentally detecting short sedentary periods as non-wear time.
-   The rolling window went from non-overlapping to overlapping (15 minute steps of
a 60 minute window results in a longsize window overlap between step of 22.5 minutes)
to improve the time resolution.

**nonwear_approach = "2023"**

The 2023 version of the algorithm uses the criteria applied to the longsize window
to assign a nonwear score to the entire longsize window (default = 60 minutes) 
being tested. Instead of centering the longsize window in the middle of the mediumsize
window, the 2023 method aligns the longsizewindow with its left edge to the left
edge of the mediumsize window. As a result, each point in time is classified multiple
times given that multiple steps of the rolling window classification will overlap
with it. If the nonwear criteria are met for any
of these windows that overlap with a point in time, it will be labelled as nonwear. 

### Additional non-wear

Inspection of unpublished data on non-wear classification
by the algorithm as described in our published work indicated that the
algorithm does not cope well with periods of monitor transportation per
post. Here, long periods of non-wear are briefly interrupted by periods
of movement, which are normally interpreted as monitor wear. Therefore,
the algorithm was expanded with an additional stage in which the
plausibility of "wear-periods" in-between non-wear periods is tested.
Short periods of detected wear-time in-between longer periods of
detected non-wear were classified as non-wear time based on the duration
and the proportion of the duration relative to the bordering periods of
detected non-wear-periods. The following criteria were derived from
visual observation of various datasets using knowledge about study
protocols. All detected wear-periods of less than six hours and less
than 30% of the combined duration of their bordering non-wear periods
were classified as non-wear. Additionally, all wear-periods of less than
three hours and which formed less than 80% of their bordering non-wear
periods were classified as non-wear. The motivation for selecting a
relatively high criteria (\< 30%) in combination with a long period
(6hrs) and a low criteria (\< 80%) in combination with a short period (3
hrs) was that long period are more likely to be actually related to
monitor wear time. A visual model was created, see picture below. Here, units
of time are presented in squares and marked grey if detected as non-wear
time. Period C is detected as wear-time and borders to non-wear periods
B and D. If the length of C is less than six hours and C
divided by the sum of B and D is less than 0.3 then the first criteria
is met and block C is turned into a non-wear period.

```{r, out.width = "400px",echo=FALSE}
knitr::include_graphics("nonwearimage.jpg")
```

### Beginning and ending of the recording

By visual inspection of \>100 traces from a large observational study it
turned out that applying this stage in three iterative stages allowed
for improved classification of periods characterised by intermittent
periods of non-wear and apparent wear. Further, an additional rule was
introduced for the final 24 hours of each measurement. The final 24
hours are often considered the period in which the accelerometer is
potentially taken off but moved because of transportation, e.g. by the
mail service. All wear-periods in the final 24 hrs of each measurement
shorter than three hours and preceded by at least one hour of non-wear
time were classified as non-wear. 

Finally, if the measurement starts or
ends with a period of less than three hours of wear followed by non-wear
(any length) then this period of wear is classified as non-wear. These
additional criteria for screening the beginning and end of the
accelerometer file reflect the likelihood of movements that are involved
when starting the accelerometer or downloading the data from the
accelerometer. This final check can be turned off with
argument `nonWearEdgeCorrection`.

Reference:

-   van Hees VT, Gorzelniak L, Dean León EC, Eder M, Pias M, Taherian S,
    Ekelund U, Renström F, Franks PW, Horsch A, Brage S. Separating
    movement and gravity components in an acceleration signal and
    implications for the assessment of human daily physical activity.
    [PLoS One. 2013 Apr 23](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061691).

Key decisions to be made:

1.  Size of windows
2.  Whether to utilize the non-wear detection
3.  Non-wear approach (either 2013 or 2023)

Key output variables:

1.  Raw classification
2.  Non-wear duration
3.  Non-wear duration taking into account the protocol

## Clipping score

GGIR also screens the acceleration signal for "clipping". If more than 50% of
the data points in a mediumsize window (default = 15 minutes) are close
to the maximal dynamic range of this sensor the corresponding time
period is considered as potentially corrupt data, which may be explained
by the sensor getting stuck at its extreme value. For example, for a dynamic range of 8*g*, 
accelerations over 7.5*g* would be marked as "clipping". 

Reference:

-   van Hees VT, Gorzelniak L, Dean León EC, Eder M, Pias M, Taherian S,
    Ekelund U, Renström F, Franks PW, Horsch A, Brage S. Separating
    movement and gravity components in an acceleration signal and
    implications for the assessment of human daily physical activity.
    [PLoS One. 2013 Apr 23](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061691)

## Why collapse information to epoch level?

Although many data points are collected we decide to only work with
aggregated values (e.g. 1 or 5 second epochs) for the following reasons:

1.  Accelerometers are often used to describe patterns in metabolic
    energy expenditure. Metabolic energy expenditure is typically
    defined per breath or per minute (indirect calorimetry), per day
    (room calorimeter), or per multiple days (doubly labelled water
    method). In order to validate our methods against these reference
    standards we need to work with a similar time resolution.

2.  Collapsing the data to epoch summary measures helps to standardise
    for differences in sample frequency between studies.

3.  There is little evidence that the raw data is an accurate
    representation of body acceleration. All scientific evidence on the
    validity of accelerometer data has so far been based on epoch
    averages.

4.  Collapsing the data to epoch summary measures may help to average
    out different noise levels and make sensor brands more comparable.

### Why does the first epoch not allign with the original start of the recording

GGIR uses short (default 5 seconds) and long epochs (default 15
minutes). The epochs are aligned to the hour in the day, and to each
other. For example, if a recording starts at 9:52:00 then the GGIR will
work with epochs derived from 10:00:00 onward. If the recording starts
at 10:12 then GGIR will work with epochs derived from 10:15:00 onward.

Motivation:

-   This allows us to have a standardised time grid across recordings to
    describe behaviour.
-   This allows us to calculate behaviour exactly per day or per
    specified time interval in a day.

If the first 15 minute epochs would start at 9:52 then the next one
would start at 10:07, which makes it impossible to make statement about
behaviour between 10:00 and 13:00.

## Sleep analysis {#Sleep_analysis}

In GGIR sleep analysis has been implemented in part 3 and 4. Sleep
analysis comes at two levels: The identification of the main Sleep
Period Time (SPT) window or the time in bed window (TIB), and the
discrimination of sleep and wakefulness periods. The term sleep is
somewhat controversial in the context of accelerometry, because
accelerometer only capture lack of movement. To acknowledge this
challenge GGIR refers to these classified sleep periods as **sustained
inactivity bouts (abbreviated as SIB)**.

Current, GGIR offers the user the choice to identify SIB period using
any of the following algorithms:

- **vanHees2015:** Heuristic algorithm proposed in 2015
[link](https://doi.org/10.1371/journal.pone.0142533) which looks for periods of
time where the z-angle does not change by more than 5 degrees for at least 5 minutes.
This in contrast to conventional sleep detection algorithms such as Sadeh, Galland,
and ColeKripke which rely on acceleration to estimate sleep. The vanHees2015
algorithm is the default.
- **Sadeh1994:** The algorithm proposed by Sadeh et al.
[link](https://doi.org/10.1093/sleep/17.3.201).
To use the GGIR implementation of the zero-crossing counts and Sadeh algorithm,
specify argument `HASIB.algo = "Sadeh1994"` and argument `Sadeh_axis = "Y"` to
indicate that the algorithm should use the Y-axis of the sensor.
- **Galland2012:** The count-scaled algorithm proposed by Galland et al.
 [link](http://dx.doi.org/10.1016/j.sleep.2012.01.018). To use our implementation
 of the Galland2012 algorithm specify argument `HASIB.algo = "Galland2012"`.
 Further, set `Sadeh_axis = "Y"` to specify that the algorithm should use the Y-axis.
- **ColeKripke1992:** The algorithm proposed by Cole et al.
[link](https://doi.org/10.1093/sleep/15.5.461), more specifically GGIR uses the
algortihm proposed in the paper for 10-second non-overlapping epochs with counts e
xpressed average per minute. We skip the re-scoring steps as the paper showed
marginal added value of this added complexity. To use the GGIR implementation of
the zero-crossing counts and Sadeh algorithm, specify argument
`HASIB.algo = "ColeKripke1992"` and argument `Sadeh_axis = "Y"` to indicate that
the algorithm should use the Y-axis of the sensor.
- **NotWorn:** This algorithm can be used if the study protocol was to not wear
the accelerometer at night. GGIR will then look for hours with close to zero
movement and treat those as sustained inactivity bouts. It should be obvious that
this does not facilitate any meaningful sleep analysis but it does allow GGIR to 
then run GGIR part 5 based on the assumption that this analysis helped to isolate 
night time from daytime.


### Notes on sleep classification algorithms designed for count data


#### Replication of the movement counts needed

The implementation of the zero-crossing count in GGIR is not an exact copy of
the original approach as used in the AMA-32 Motionlogger Actigraph by
Ambulatory-monitoring Inc. ("AMI") that was used in the studies by Sadeh, Cole,
Kripke and colleagues in late 1980s and 1990s. No complete publicly accessible
description of that approach exists. From personal correspondence with AMI, we
learnt that the technique has been kept proprietary and has never been shared
with or sold to other actigraphy manufacturers (time of correspondence October 2021).
Therefore, if you would like to replicate the exact zero-crossing counts calculation
used by Sadeh and colleague's consider using AMI's actigraph device (you will
  have to trust AMI that hardware has not changed since the 1980s). However, if
  you prioritise openness over methodological consistency with the original
  studies by Sadeh, Cole, and colleagues then you may want to consider any of
  the open source techniques in this library.

#### Missing information for replicating movement counts

More specifically, the missing information about the calculation includes:
(1) Sadeh specified that calculations were done on data from the Y-axis but
the direction of the Y-axis was not clarified. Therefore, it is unclear whether
the Y-axis at the time corresponds to the Y-axis of modern sensors,
(2) Properties of the frequency filter are missing like the filter order and
more generally it is unclear how to simulate original acceleration sensor
behaviour with modern sensor data, and (3) Sensitivity of the sensor, we are
now guessing that the Motionlogger had a sensitivity of 0.01 _g_ but without
direct proof.

The method proposed by Galland and colleagues in 2012 was designed for counts
captured with the Actical device (Mini Mitter Co, Inc Bend OR). Based on the
correspondence with AMI we can conclude that Actical counts are not identical
to AMI's actigraph counts. Further, a publicly accessible complete description
of the Actical calculation does not exist. Therefore, we can also conclude that
methodological consistency cannot be guaranteed for Actical counts.

#### An educated guess and how you can to help optimise the implementation

Following the above challenges the implementation of the zero-crossing count in
GGIR is based on an educated guess where we used all information we could find
in literature and product documentation. In our own evaluation the zero-crossing
count value range looks plausible when compared to the value range in the
original publications.

**How you can help to optimise the implementation:**

Given the uncertainties surrounding the older sleep algorithm we encourage GGIR
users to evaluate and help optimise the algorithms. Here, we have the following
suggestions:

- For ActiGraph users, when comparing GGIR Cole-Kripke estimates with ActiLife Cole
Kripke estimate be aware that ActiLife may have adopted a different Cole-Kripke
algorithm as the original publication presented four algorithms. Further, ActiLife
may have used different educated guesses about how Motionlogger counts are calculated.
- Compare GGIR sleep estimate with Polysomnography or Motionlogger output and
try to optimise the zero crossing count parameters as discussed below.

**Input argument to aid in the optimisation:**

To aid research in exploring count type algorithms, we also implemented the
`brondcounts` as proposed by Br&#248;nd and Brondeel and available via R package
activityCounts, as well as the `neishabouricounts` that follows the algorithm implemented
in the close-source software ActiLife by ActiGraph and is available in the R package
actilifecounts. DISCLAIMER: the brondcounts option has been deprecated as of 
October2022 due to issues with the activityCounts package it relies on. We will 
reactivate brondcounts once the issues are resolved.
To extract these metrics in addition to the zero crossing count,
specify `do.brondcounts = TRUE` and/or `do.neishabouricounts = TRUE` which is used 
in GGIR part 1 and uses R packages activityCounts and actilifecounts in the background. 
As a result, sleep estimates for Sadeh, Cole-Kripke
or Galland will be derived based on the zero crossing algorithm and additionally on
the `brondcounts` or/and `actilifecounts` algorithms if requested by the user. 
Further, we have added parameters to help modify
the configuration of the zero-crossing count calculation, see arguments:
`zc.lb`, `zc.hb`, `zc.sb`, `zc/order`, and `zc.scale`. As well as one parameter to modify the
neishabouricounts calculation, see argument: `actilife_LFE`.

### Guiders

SIBs (explained above) can occur anytime in the day. In order to
differentiate SIBs that correspond to daytime rest/naps from SIBs that
correspond to the main Sleep Period Time window (abbreviated as SPT), a
guiding method referred as **guider** is used. All SIBs that overlap
with the window defined by guider are considered as sleep within the SPT
window. The start of the first SIB identified as sleep period and the
end of the last SIB identified as sleep period define the beginning and
the end of the SPT window. In this way the classification relies on the
accelerometer for detecting the timing of sleep onset and waking up
time, but the guider tells it in what part of the day it should look, as
SPT window will be defined only if SIB is detected during the guider
specified window.

If a guider reflects the Time in Bed the interpretation of the Sleep
Period Time, Sleep onset time and Wakeup time remains unchanged.
However, we can then also assess sleep latency and and sleep efficiency,
which will be included in the report.

The guiding method as introduced above can be one of the following
methods:

-   **Guider = sleep log**: As presented in [before mentioned 2015
    article](https://doi.org/10.1371/journal.pone.0142533).See section
    [on sleep analysis related
    arguments](#key-arguments-related-to-sleep-analysis) for a
    discussion fo sleep log data formats. Specify argument
    `sleepwindowType` to clarify whether the sleeplog capture
    "TimeInBed" or "SPT". If it is set to "TimeInBed", GGIR will
    automatically expand its part 4 analyses with sleep latency and
    sleep efficiency assessment.
-   **Guider = HDCZA**: As presented in [our 2018
    article](https://dx.doi.org/10.1038/s41598-018-31266-z). The HDCZA
    algorithm does not require access to a sleep log, and is designed
    for studies where no sleep log is available. The time segment over
    which the HDCZA is derived are by default from noon to noon.
    However, if the HDCZA ends between 11am and noon then it will be
    applied again but to a 6pm-6pm window.
-   **Guider = L5+/-12**: As presented in [our 2018
    article](https://dx.doi.org/10.1038/s41598-018-31266-z). Twelve hour
    window centred around the least active 5 hours of the day.
-   **Guider = setwindow**: Window times are specified by user, constant
    at specific clock times with argument `def.noc.sleep`.
-   **Guider = HorAngle**: Only used if argument
    `sensor.location="hip"`, because this will trigger the
    identification of the longitudinal axis based on 24-hour lagged
    correlation. You can also force GGIR to use a specific axis as
    longitudinal axis with argument `longitudinal_axis`. Next, it
    identifies when the horizontal axis is between -45 and 45 degrees
    and considers this a horizontal posture. Next, this is used to
    identify the largest time in bed period, by only considering
    horizontal time segments of at least 30 minutes, and then looking
    for longest horizontal period in the day where gaps of less than 60
    minutes are ignored. Therefore, it is partially similar to the HDCZA
    algorithm. When "HorAngle" is used, `sleepwindowType` is
    automatically set to "TimeInBed".
-   **Guider = NotWorn**: Used for studies where the instruction was
    not to wear the accelerometer durating the night. GGIR then searches
    for the longest period with zero movement.

For all guiders other than "HorAngle" and "sleep log" argument
`sleepwindowType` is automatically switched to "SPT", such that no
attempt is made to estimate sleep latency or sleep efficiency.

GGIR uses by default the sleep log, if the sleep log is not available it
falls back on the HDCZA algorithm (or HorAngle if
`sensor.location="hip"`). If HDCZA is not successful GGIR will falls
back on the L5+/-12 definition, and if this is not available it will use
the setwindow. The user can specify the priority with argument
`def.noc.sleep`. So, when we refer to guider then we refer to one of
these five methods.

### Daysleepers (nights workers)

If the guider indicates that the person woke up after noon, the sleep
analysis in part 4 is performed again on a window from 6pm-6pm. In this
way our method is sensitive to people who have their main sleep period
starting before noon and ending after noon, referred as daysleeper=1 in
daysummary.csv file, which you can interpret as night workers. Note that
the L5+/-12 algorithm is not configured to identify daysleepers, it will
only consider the noon-noon time window.

### Cleaningcode {#Cleaningcode}

To monitor possible problems with the sleep assessment, the variable
**cleaningcode** is recorded for each night. Cleaningcode per night
(noon-noon or 6pm-6pm as described above) can have one of the following
values:

-   0: no problem, sleep log available and SPT is identified;
-   1: sleep log not available, thus HDCZA is used and SPT is
    identified,
-   2: not enough valid accelerometer data based on the non-wear and
    clipping detection from part summarised over the present night where
    the argument `includenightcrit` indicates the minimum number of
    hours of valid data needed within those 24 hours.
-   3: no accelerometer data available,
-   4: there were no nights to be analysed for this person,
-   5: SPT estimated based on guider only, because either no SIB was
    found during the entire guider window, which complicates defining
    the start and end of the SPT, or the user specified the ID number of
    the recording and the night number in the
    [data_cleaning_file](#Data_cleaning_file) to tell GGIR to rely on
    the guider and not rely on the accelerometer data for this
    particular night
-   6: no sleep log available and HDCZA also failed for this specific
    night then use average of HDCZA estimates from other nights in the
    recording as guider for this night. If HDCZA estimates are not
    available during the entire recording then use L5+/-12 estimate for
    this night. The last scenario seems highly unlikely in a recording
    where the accelerometer was worn for at least one day.

### Difference between cleaned and full output

All the information for each night is stored in the `results/QC` folder
allowing tracing of the data analysis and night selection. The cleaned
results stored in the results folder. In part 4 a night is excluded from
the 'cleaned' results based on the following criteria:

-   If the study proposed a sleep log to the individuals, then nights
    are excluded for which the sleep log was not used as a guider
    (i.o.w. nights with cleaningcode not equal to 0 or variable sleep
    log used equals FALSE).
-   If the study did not propose a sleep log to the individuals, then
    all nights are removed with cleaningcode higher than 1.

Be aware that if using the full output and working with wrist
accelerometer data, then missing entries in a sleep log that asks for
Time in Bed will be replaced by HDCZA estimates of SPT. Therefore, extra
caution should be taken when working with the full output.

Notice that part 4 is focused on sleep research, by which the cleaned
reported is the way it is. In the next section we will discuss the
analysis done by part 5. There, the choice of guider may be considered
less important, by which we use different criteria for including nights.
So, you may see that a night that is excluded from the cleaned results
in part 4 still appears in the cleaned results for part 5.

### Data cleaning file {#Data_cleaning_file}

The package allows some adjustments to be made after data quality check.
The `data_cleaning_file` argument allows you to specify individuals and
nights for whom part4 should entirely rely on the guider (for example if
we decide to use sleep log only information). The first column of this
file should have header `ID` and there should be a column
`relyonguider_part4` to specify the night. The `data_cleaning_file`
allows you to tell GGIR which person(s) and night(s) should be omitted
in part 4. The the night numbers to be excluded should be listed in a
column `night_part4` as header.

## Waking-waking or 24 hour time-use analysis {#Waking-waking_or_24_hour}

In part 5 the sleep estimates from part 4 are used to describe 24-hour
time use. Part 5 allows you to do this in two ways: Literally 24 hours
which start and end a calendar day (default midnight, but modifiable
with argument `dayborder`) or from waking up to waking up. In GGIR we
refer to the former as **MM** windows and to the latter as **WW**
windows. The onset and waking times are guided by the estimates from
part 4, but if they are missing part 5 will attempt to retrieve the
estimate from the guider method, because even if the accelerometer was
not worn during the night, or a sleep log is missing in a study where
sleep log was proposed to the participants, estimates from a sleep log
or HDCZA can still be considered a reasonable estimate of the SPT window
in the context of 24-hour time use analysis.

If WW is used in combination with ignoring the first and last midnight,
argument `excludefirstlast`, then the first wake-up time (on the second
recording day) needs to be extracted for the first WW day. This is done
with the guider method. This also means that the last WW window ends on
the before last morning of the recording.

A distinction is made between the full results stored in the
`results/QC` folder and the cleaned results stored in the results
folder.

### Time series output files

If you want to inspect the time series corresponding to these windows
then see argument `save_ms5rawlevels`, which allows you to export the
time series including behavioral classes and non-wear information to csv
files. The behavioral classes are included as numbers, the legend for
these classes is stored as a separate legend file in the meta/ms5.outraw
folder named "behavioralcodes2020-04-26.csv" where the date will
correspond to the date of analysis.

**Additional input arguments that may be of interest:**

-   `save_ms5raw_format` is a character string to specify how data
    should be stored: either "csv" (default) or "RData". Only used if
    save_ms5rawlevels=TRUE.
-   `save_ms5raw_without_invalid` is Boolean to indicate whether to
    remove invalid days from the time series output files. Only used if
    save_ms5rawlevels=TRUE.

**The time series output file comes with the following columns:**

| Column name          | Description        |
|------------------------|-----------------------------------------------|
| SleepPeriodTime      | Is 1 if SPT is detected, 0 if not. Note that this refers to the combined usage of guider and detected sustained inactivity bouts (rest periods).|
| invalidepoch         | Is 1 if epoch was detect as invalid (e.g. non-wear), 0 if not.       |
| guider   | Is 1 if guider method detect epoch as SPT (e.g. sleeplog, HDCZA), 0 if not. You will not find here which guider is used, for this see other GGIR output.    |
| window   | Numeric indicator of the analysis window in the recording. If timewindow = "MM" then these correspond to calendar days, if timewindow = "WW" then these correspond to which wakingup-wakingup window in the recording, if timewindow = "OO" then these correspond to which sleeponset-sleeponset window in the recording. So, in a recording of one week you may find window numbers 1, 2, 3, 4, 5 and 6.|
| class_id | The behavioural class codes are documented in the exported csv file meta/ms5outraw/behaviouralcodes.csv. Class codes above class 8 will be analysis specific, because it depends on the number time variants of the bouts used. For example, if you look at MVPA lasting 1-10, 10-20, 30-40 then all of them will have their own class_id. |
| invalid_fullwindow   | Fraction of the window (see above) that represents invalid data. I added this to make it easier to filter the timeseries based on whether days are valid or not.         |
| invalid_sleepperiod  | Fraction of SPT within the current window that represents invalid data.          |
| invalid_wakinghours  | Fraction of SPT within the current window that represents invalid data.          |

To familiarize yourself with how the variables it can be helpful to plot
them yourself.

For users we also want to export the time serie sof multiple metric
values see argument `epochvalues2csv` which relates to the storage of
time series in GGIR part 2.

### Day inclusion criteria

The full part 5 output is stored in the `results/QC` folder. The default
inclusion criteria for days in the cleaned output from part 5 (stored in
the `results` folder) are:

-   For both MM and WW defined days: The valid (sensor worn) time
    fraction of the day needs to be above the fraction specified with
    argument `includedaycrit.part5` (default 2/3).
-   For MM defined days only: The length of the day needs to be at least
    the number of hours as specified by `minimum_MM_length.part5`
    (default 23). Note that if your experiment started and ended in the
    middle of the day then this default setting will exclude those
    incomplete first and last days. If you think including these days is
    still meaningful for your work then adjust the argument
    `minimum_MM_length.part5`.

**Important notes:**

-   No criteria is set for the amount of valid data during the SPT
    window, because all we are interested in part 5 is knowing the
    borders of the night and we trust that this was sufficiently
    estimated by part 4. If you disagree then please notice that all the
    days are included in the full report available in `results/QC`
    folder.
-   This means that argument `includenightcrit` as used for part 4 is
    not used in part 5.

The `data_cleaning_file` argument discussed in
[Data_cleaning_file](#Data_cleaning_file) also allows you to tell GGIR
which person(s) and day(s) should be omitted in part 5. The the day
numbers to be excluded should be listed in a column `day_part5` as
header.

### Fragmentation metrics

When setting input argument as `frag.metrics="all"` GGIR part 5 will
perform behavioural fragmentation analysis for daytime and (separately) for spt.
Do this in combination with argument `part5_agg2_60seconds=TRUE` as that will
aggregate the time series to 1 minute resolution as is common in
behavioural fragmentation literature.

In GGIR, a fragment for daytime is a defined as a sequence of epochs that belong to
one of the four categories:

1.  Inactivity
2.  Light Physical Activity (LIPA)
3.  Moderate or Vigorous Physical Acitivty (MVPA)
4.  Physical activity (can be either LIPA or MVPA)

Each of these categories represents the combination of bouted and
unbouted time in the respective categories. Inactivity and physical
activity add up to a full day (outside SPT), as well as inactivity, LIPA and MVPA. The
fragmentation metrics are applied in function `g.fragmentation`.

A fragment of SPT is defined as a sequence of epochs that belong to
one of the four categories:
1. Estimated sleep
2. Estimated wakefulness
3. Inactivity
4. Physical activity (can be either LIPA or MVPA)

Note that from the metrics below only fragmentation metrics TP and NFrag are 
calculated for the SPT fragments.

**Literature about these metrics:**

-   Coefficient of Variance (`CoV`) is calculated according to [Blikman
    et al.
    2014](https://doi.org/10.1016/j.apmr.2014.08.023).
-   Transition probability (`TP`) from Inactivity (IN) to Physical
    activity (IN2PA) and from Physical activity to inactivity (PA2IN)
    are calculated as 1 divided by the mean fragment duration. The
    transition probability from Inactivity to LIPA and MVPA are
    calculated as: (Total duration in IN followed by LIPA or MVPA,
    respectively, divided by total duration in IN) divided by the
    average duration in IN.
-   Gini index is calculated with function `Gini` from the `ineq` R
    package, and with it's argument `corr` set to TRUE.
-   Power law exponent metrics: Alpha, x0.5, and W0.5 are calculated
    according to [Chastin et al.
    2010](https://doi.org/10.1016/j.gaitpost.2009.09.002).
-   Number of fragment per minutes (`NFragPM`) is calculated identical
    to metric `fragmentation` in [Chastin et al.
    2012](https://academic.oup.com/ageing/article/41/1/111/46538), but
    it is renamed here to be a more specific reflection of the
    calculation. The term `fragmentation` appears too generic given that
    all fragmentation metrics inform us about fragmentation. Please not
    that this is effectively the same metric as the transition
    probability, because total number divided by total sum in duration
    equals 1 divided by average duration. This is just different
    terminology for the same construct.

**Conditions for calculation and value when condition is not met:**

-   Metrics `Gini` and `CoV` are only calculated if there are at least
    10 fragments (e.g. 5 inactive and 5 active). If this condition is
    not met the metric value will be set to missing.
-   Metrics related to power law exponent alpha are also only calculated
    when there are at least 10 fragments, but with the additional
    condition that the standard deviation in fragment duration is not
    zero. If these conditions are not met the metric value will be set
    to missing.
-   Other metrics related to binary fragmentation (`mean_dur_PA` and
    `mean_dur_IN`), are calculated when there are at least 2 fragments
    (1 inactive, 1 active). If this condition is not met the value will
    is set to zero.
-   Metrics related to `TP` are calculated if: There is at least 1
    inactivity fragment AND (1 LIPA OR 1 MVPA fragment). If this
    condition is not met the `TP` metric value is set to zero.

To keep an overview of which recording days met the criteria for
non-zero standard deviation and at least ten fragments, GGIR part5
stores variable `Nvaliddays_AL10F` at person level (=Number of valid
days with at least 10 fragments), and `SD_dur` (=standard deviation of
fragment durations) at day level as well as aggregated per person.

**Difference between fragments and blocks:**

Elsewhere in the part5 we use the term `block`. A `block` is a sequence
of epochs that belong to the same behavioural class. This may sound
similar to the definition of a fragment, but for blocks we distinguish
every behavioural class, which includes the subcategories such as bouted
and unbouted behaviour. This means that variables `Nblock_day_total_IN`
and `Nblock_day_total_LIG` are identical to `Nfrag_IN_day` and
`Nfrag_LIPA_day`, respectively. In contrast, for fragments we may group LIPA and MVPA
together when refering to the fragmentation of PA.

**Differences with R package ActFrag:**

The fragmentation functionality is loosely inspired on the great work
done by Dr. Junrui Di and colleages in R package ActFrag, as described
in [Junrui Di et al.
2017](https://www.biorxiv.org/content/10.1101/182337v1).

However, we made a couple of a different decisions that may affect
comparability:

-   GGIR derives fragmentation metrics per day. This allows us to avoid
    the issue of gaps between days that need to be dealt with. Further, it allows us to
    test for behavioural differences between days of the week. It is
    well known that human behaviour can be driven by weekly rhythms,
    e.g. work days versus weekend. Estimating fragmentation per day of
    the week allows us to study and account for such possible variation.
    As with all other GGIR variables we also report recording level
    aggregates of the daily estimates.
-   Transition probability is according
    to [Lim et al. 2011](https://doi.org/10.5665/sleep.1400)
-   Power law alpha exponent metrics were calculated according to
    [Chastin et al.
    2010](https://doi.org/10.1016/j.gaitpost.2009.09.002)
    using the theoretical minimum fragment duration instead of the
    observed minimum fragment duration.

## Why use data metric ENMO as default?

GGIR offers a range of acceleration metrics to choose from, but only one
metric can be the default. Acceleration metric ENMO (Euclidean Norm
Minus One with negative values rounded to zero) has been the default
metric in GGIR. In 2013 we wrote a paper in which we investigated
different ways of summarising the raw acceleration data. In short,
different metrics exist and there is very little literature to support
the superiority of any metric at the time. As long as different studies
use different metrics their findings will not be comparable. Therefore,
the choice for metric ENMO is partially pragmatic. GGIR uses ENMO as
default because:

1.  ENMO has demonstrated value in describing variance in energy
    expenditure, correlated with questionnaire data, able to describe
    patterns in physical activity
2.  ENMO is easy to describe mathematically and by that improves
    reproducibility across studies and software tools
3.  ENMO attempts to quantify the actual biomechanical acceleration in
    universal units.
4.  The 2013 paper showed that when ENMO is used in combination with
    auto-calibration it has similar validity to filter-based metrics
    like HFEN and BFEN, which are conceptually similar to metrics
    proposed later such as MIMSunit, MAD, AI0.
5.  Studies who have criticised ENMO consistently failed to apply
    auto-calibration, or attempted to apply auto-calibration in a lab
    setting, ignoring the fact that the auto-calibration is not designed
    for short lasting lab settings. It needs free-living data to work
    properly. Further, studies are often not clear about how the
    problematic zero imputation during the idle sleep mode in ActiGraph
    devices is dealt with. See for a more detailed discussion on this
    the paragraph: [Published cut-points and how to use
    them](#Published%20cut-points%20and%20how%20to%20use%20them).

See also
[this](https://medium.com/@vincentvanhees/ten-misunderstandings-surrounding-information-extraction-from-wearable-accelerometer-data-a4f767a865b6)
blog post on this topic.

## What does GGIR stand for?

I wanted a short name and not to spend too much time finding it. At the
time I was primarily working with GENEActiv and GENEA data In R, and
that's how the name GGIR was born: Short, easy to remember, and as
acronym sufficiently vague to not be tight up with a specific
functionality. However, later the functionality expanded to other sensor
brands, so the abbreviation has lost its functional meaning.

## Circadian Rhythm analyses

### MXLX

Detection of the continuous least (LX) and most (MX) active X hours in a day, where X
is defined by argument `winhr`. For both GGIR calculates the average
acceleration, the start time, and if argument `iglevels` is specified
also the intensity gradient. If argument `winhr` is a vector then
descriptive values for LX and MX are derived per value in `winhr`.
Within GGIR part 2 MXLX is calculated per calendar day and, if argument
`qwindow` is specified, per segment of the day. Within GGIR part 5 MXLX
is calculated per window, and if used in combination with the GENEActiv or Axivity
accelerometer brand LUX estimates per LX and MX are included.

The MX metric describe here should not be confused by the MX metrics as proposed
by [Rowlands et al.](https://doi.org/10.1186/s40798-019-0225-9) which looks at
accumulated most active time which may not always be continuous in time.
The MX metrics by Rowlands et all are discussed further down.

### Cosinor analysis and Extended Cosinor analysis

The (Extended) Cosinor analysis quantifies the circadian 24 hour cycle.
To do this GGIR uses R package
[ActCR](https://CRAN.R-project.org/package=ActCR) as a dependency.
Specify argument `cosinor = TRUE` to perform these analysis.

The implementation within GGIR part 2 is as follows:

-   Acceleration values are averaged per minute, and then log
    transformation as `log(acceleration converted to _mg_ + 1)`.
-   Invalid data points such as caused by non-wear are set to missing
    (NA) in order to prevent the imputation approach used elsewhere in
    GGIR to influence the Cosinor analysis. We do this because
    imputation technique generally come with some assumptions about
    circadian rhythm.
-   GGIR looks for the first valid data point in the recording and then
    selects the maximum integer number of recording days following this
    data point and feeds these to the ActCosinor and ActExtendCosinor
    functions of ActCR. The time offset between the start and the
    following midnight is then used to reverse offset the ActCR results,
    to ensure that acrophase and acrotime can be interpreted relative to
    midnight.
-   In relation to Day Saving Time: Duplicated time stamps when clock
    moves backward are ignored and missing time stamps when clock moves
    forward are inserted as missing values.
-   Time series corresponding to the fitted models are stored inside the
    part 2 milestone data to facilitate visual inspection. For the
    moment they are not used in any GGIR visualisation, but you may want
    to look them up and try to plot them yourself.

### Intradaily Variability (IV) and Interdaily Stability (IS)

#### IV and IS - Default

The original implementation (argument IVIS.activity.metric = 1) uses the
continuous numeric acceleration values. However, as we later realised
this is not compatible with the original approach by van Someren and
colleagues, which uses a binary distinction between active and inactive.
Therefore, a second option was added (argument IVIS.activity.metric = 2),
which needs to be used in combination with accelerometer metric ENMO,
and collapses the acceleration values into a binary score of rest versus
active. This is the current default.

#### IV and IS - Cosinor analysis compatible

Disclaimer: The following has been implemented 2022, but is currently 
undergoing critical evaluation. As a result, we may update this algorithm 
during the course of 2023.

IS is sometimes used as a measure of behavioural robustness when
conducting Cosinor analysis. However, to work with the combination of
the two outcomes it seems important that IS is calculated from the same
time series. Therefore, when `cosinor = TRUE` IV and IS are calculated
twice: Once as part of the default IV and IS analysis as discussed
above, and once as part of the Cosinor analysis using the same log
transformed time series. More specifically, the IV and IS algorithm is
applied with `IVIS.activity.metric = 2` and
`IVIS_acc_threshold = log(20 + 1)` to make the binary distinction
between active and inactive, and `IVIS_per_daypair = TRUE`. The setting
`IVIS_per_daypair` was specifically designed for this context to handle
the potentially missing values in the time series as used for Cosinor
analysis. Applying the default IVIS algorithm would not be able to
handle the missing values and would result in a loss of information if
all non-matching epochs across the entire recording were excluded.
Instead, IV and IS are calculated as follows:

1.  Per day pair based on matching valid epochs only IV and IS and
    calculated. Here, a log is kept of the number of valid epoch per day
    pair.
2.  Omit day pairs where the fraction of valid epoch pairs is below 0.66
    (0.66 is hard-coded at the moment).
3.  Calculate average IS across days weighted by fraction of valid
    epochs per day pairs.

The new Cosinor-compatible IV and IS estimates are stored as output
variables `cosinorIV` and `cosinorIS`.


## ActiGraph's idle sleep mode

The idle sleep mode is [explained](https://actigraphcorp.my.site.com/support/s/article/Idle-Sleep-Mode-Explained)
on the manufacturer's website. In short, idle sleep mode is a setting that can
be turned on or off by the user. When it is turned on the device
will fall asleep during periods of no movement, resulting in time gaps in the data.
This functionality was probably introduced to safe battery life and minimize data size.
However, it also means that we end up with time gaps that need to be accounted for.

### Time gap imputation

Studies done with ActiGraph devices when configured with 'idle sleep
mode' and with data exported to .csv by the commerical ActiLife software will have
imputed values in all three axes  during periods of no movement. Note that the
imputation by the ActiLife software has changed at some point in time. Initially the
imputation was zeros but with more recent versions of ActiLife the imputation uses
the last recorded value for each axes.

When processing gt3x files that have time gaps GGIR takes care of the time gap
imputation: Time gaps shorter than 90 minutes are imputed at raw data level with the
last known recorded  value before the timeg gap, while longer time gaps are imputed at
epoch level. We do this to make the data processing more memory efficient and faster.

Time gaps in the data are considered non-wear time in GGIR, this implies that we 
trust the ActiGraph sleep mode to only be activated when the device is not worn, 
although there is always a risk of sleep/sedentary misclassification.

### The importance of reporting idle.sleep.mode usage

Studies often forget to clarify whether idle sleep mode was used
and if so, how it was accounted for in the data processing.
Especially, the insertion of zero strings is problematic as raw data accelerometers
should always measure the gravitational component when not moving.
This directly impacts metrics that rely on the presence of a gravitational
component such as ENMO, EN, ENMOa, SVMgs, and angles. Further, also other metrics may
be affected as the sudden disappearance of gravitational acceleration will cause a
spike at the start and end of the idle sleep mode period. More
generally speaking, we advise ActiGraph users to:

- Disable the 'idle sleep mode' as it harms the transparency and reproducibility
since no mechanism exists to replicate it in other accelerometer brands, and it
is likely to challenge accurate assessment of sleep and sedentary behaviour.
- That data collected with 'idle sleep mode' turned on is not be referred to as
raw data accelerometry, because the data collection process has involved proprietary
pre-processing steps which violate the core principle of raw data collection.
- Report whether 'idle sleep mode' was used. If the choice was not consistent within
a study then try to account for idle mode sleep usage in the statistical analyses.

## MX metrics (minimum intensity of most active X minutes)

The `qlevels` argument (the percentile in the distribution of short epoch 
metric value) can be used to describe the accelerations that participants spend “X”
accumulated minutes a day above, described as the MX metrics
(e.g., [Rowlands et al](https://doi.org/10.1186/s40798-019-0225-9)).

The MX metrics should not be confused with the most active continuous X hours, e.g. M10, as
used in circadian rhythm research that also can be derived with GGIR, see argument `winhr`.

**Usage**
To use the MX metrics as proposed by [Rowlands et al](https://doi.org/10.1186/s40798-019-0225-9),
specify the durations of the 24h day that you wish to filter out the accelerations for.
For example, to generate the minimum acceleration value for the most active 30 minutes
you can call qlevels = (1410/1440), which will filter out the lowest 1410 minutes 
of the day. This can also be used as a nested term to generate multiple metrics, 
for example to call M60, M30 and M10, you can use argument:

`qlevels = c(c(1380/1440),c(1410/1440),c(1430/1440)).`

Note: if your qwindow is less than 24 h, e.g. the school day 
(e.g. [Fairclough et al 2020](https://doi.org/10.1111/sms.13565)) 
the denominator should be changed from 1440 (24h) accordingly, e.g. if an 8 h 
window the denominator would be 480 rather than 1440.

**Output**
The output in part2 summary files will refer to this as a percentile of the day. 
Thus, for a 24 h day, M30 will appear as “p 97.91666_ENMO_mg_0.24hr”. To graph 
the radar plots of these MX metrics as first described by 
[Rowlands et al](https://doi.org/10.1186/s40798-019-0225-9), you 
can access [this](https://github.com/Maylor8/RadarPlotGenerator) github repository
which provides the R code and detailed instructions on how to make the radar 
plots using your own data.


## Minimum recording duration {#Minimum_recording_duration}

GGIR has been designed to process multi-day recordings. The minimum recording duration
considered by GGIR depends on the type of analysis:

**Running part 1 and 2**

- File size; At least 2MB, where 2MB can be adjusted with argument minimumFileSizeMB. 
This should not be changed unless you have good reason to believe that a smaller 
file size is also acceptable.

- Recording duration: At least two long epoch windows 
(default 60 minutes) in g.readaccfile. The size of this epoch can be altered with
the second and third value of vector argument `windowsizes`, where the third should not
be smaller than the second. For example, in short lasting lab-experiments you may
find it easier to set this to `windowsizes = c(5, 600, 600)` as non-wear detection is
usually not necessary in lab studies.

**Running part 3 and 4**

- At least one night of data is expected, where a night is expected to have at least the timestamp 
for midnight. If midnight is not found the sleep detection is skipped.

**Running part 5**

- Ideally two valid consecutive nights and the waking hours in between.


## LUX sensor data processing

Although GGIR focuses on accelerometer data a few brands come with LUX data.

In part 1 GGIR calculates the peak lux per long epoch at a default resolution of 15 minutes,
which can be modified with argument windowsizes. Peak light offers a more reliable 
estimate of light exposure per time window compared with taking the average. Further,
LUX is used in the auto-calibration.

In GGIR part 2 we visualise the LUX values in the qc plot. In part 3 and 4 LUX is 
not used for sleep classification because relation between light exposure and sleep is weak.

In part 5 we calculate the mean and maximum of the peak LUX per epoch across all
waking hours of the day. Here, the mean (peak per epoch) LUX would then indicate
average light exposure per time segment, while max peak would indicate the maximum
light exposure per day. Further, we calculate the max and mean peak LUX per most 
active consecutive X hour of the day. This is intended to offer an alternative to LUX
exposure during waking hours which relies on correct sleep classification. LUX
exposure during M10 may be seen as an alternative if you are unsure whether you 
can trust the sleep classification in your data set.

# Other Resources

-   The [GGIR package manual](https://CRAN.R-project.org/package=GGIR)
    provides documentation on individual functions.
-   For general questions about how to use GGIR join our [google
    group/mailing
    list](https://groups.google.com/forum/#!forum/rpackageggir).
-   For bug reports please post them
    [here](https://github.com/wadpac/GGIR/issues).

# Citing GGIR {#CitingGGIR}

A correct citation of research software is important to make your
research reproducible and to acknowledge the effort that goes into the
development of open-source software.

To do so, please report the GGIR version you used in the text.
Additionally, please also cite:

1.  Migueles JH, Rowlands AV, et al. GGIR: A Research Community--Driven
    Open Source R Package for Generating Physical Activity and Sleep
    Outcomes From Multi-Day Raw Accelerometer Data. Journal for the
    Measurement of Physical Behaviour. 2(3) 2019. doi:
    10.1123/jmpb.2018-0063.

If your work depends on the quantification of **physical activity** then
also cite:

2.  van Hees VT, Gorzelniak L, et al. Separating Movement and Gravity
    Components in an Acceleration Signal and Implications for the
    Assessment of Human Daily Physical Activity. PLoS ONE 8(4) 2013.
    [link](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061691)
3.  Sabia S, van Hees VT, Shipley MJ, Trenell MI, Hagger-Johnson G,
    Elbaz A, Kivimaki M, Singh-Manoux A. Association between
    questionnaire- and accelerometer-assessed physical activity: the
    role of sociodemographic factors. Am J Epidemiol. 2014 Mar
    15;179(6):781-90. doi: 10.1093/aje/kwt330. Epub 2014 Feb 4. PMID:
    24500862 [link](https://pubmed.ncbi.nlm.nih.gov/24500862/)

If you used the **auto-calibration functionality** then also cite:

4.  van Hees VT, Fang Z, et al. Auto-calibration of accelerometer data
    for free-living physical activity assessment using local gravity and
    temperature: an evaluation on four continents. J Appl Physiol 2014.
    [link](https://doi.org/10.1152/japplphysiol.00421.2014)

If you used the **sleep detection** then also cite:

5.  van Hees VT, Sabia S, et al. A novel, open access method to assess
    sleep duration using a wrist-worn accelerometer, PLoS ONE, 2015
    [link](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142533)

If you used the **sleep detection without relying on sleep diary** then
also cite:

6.  van Hees VT, Sabia S, et al. Estimating sleep parameters using an
    accelerometer without sleep diary. Scientific Reports 2018. doi:
    10.1038/s41598-018-31266-z.
    [link](https://www.nature.com/articles/s41598-018-31266-z)

If you used the **sleep regularity index** then also cite:

7.  Andrew J. K. Phillips, William M. Clerx, et al. Irregular sleep/wake
    patterns are associated with poorer academic performance and delayed
    circadian and sleep/wake timing. Scientific Reports. 2017 June 12
    [link](https://www.nature.com/articles/s41598-017-03171-4).

## Copyright for GGIR logo

The copyright of the GGIR logo lies with Accelting (Almere, The
Netherlands), please contact
[v.vanhees\@acceleting.com](mailto:v.vanhees@acceleting.com){.email} to
ask for permission to use this logo.

```{r, echo=FALSE, out.width = "60%", out.extra='style="border: 0; padding:20px"'}
knitr::include_graphics("GGIR-MASTERLOGO-RGB.png")
```
